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Sample records for fabricating convoluted shaped

  1. Adapting line integral convolution for fabricating artistic virtual environment

    NASA Astrophysics Data System (ADS)

    Lee, Jiunn-Shyan; Wang, Chung-Ming

    2003-04-01

    Vector field occurs not only extensively in scientific applications but also in treasured art such as sculptures and paintings. Artist depicts our natural environment stressing valued directional feature besides color and shape information. Line integral convolution (LIC), developed for imaging vector field in scientific visualization, has potential of producing directional image. In this paper we present several techniques of exploring LIC techniques to generate impressionistic images forming artistic virtual environment. We take advantage of directional information given by a photograph, and incorporate many investigations to the work including non-photorealistic shading technique and statistical detail control. In particular, the non-photorealistic shading technique blends cool and warm colors into the photograph to imitate artists painting convention. Besides, we adopt statistical technique controlling integral length according to image variance to preserve details. Furthermore, we also propose method for generating a series of mip-maps, which revealing constant strokes under multi-resolution viewing and achieving frame coherence in an interactive walkthrough system. The experimental results show merits of emulating satisfyingly and computing efficiently, as a consequence, relying on the proposed technique successfully fabricates a wide category of non-photorealistic rendering (NPR) application such as interactive virtual environment with artistic perception.

  2. Patterned fabric defect detection via convolutional matching pursuit dual-dictionary

    NASA Astrophysics Data System (ADS)

    Jing, Junfeng; Fan, Xiaoting; Li, Pengfei

    2016-05-01

    Automatic patterned fabric defect detection is a promising technique for textile manufacturing due to its low cost and high efficiency. The applicability of most existing algorithms, however, is limited by their intensive computation. To overcome or alleviate the problem, this paper presents a convolutional matching pursuit (CMP) dual-dictionary algorithm for patterned fabric defect detection. A preprocessing with mean sampling is performed to eliminate the influence of background texture of fabric defects. Subsequently, a set of defect-free image blocks are selected as a sample set by sliding window. Dual-dictionary and sparse coefficiencies of the defect-free sample set are obtained via CMP and the K-singular value decomposition (K-SVD) based on a Gabor filter. Then we employ the defect-free and defective fabric image's projections onto the dual-dictionary as features for defect detection. Finally, the test results are determined by comparing the distance between the features to be measured. Experimental results reveal that the proposed algorithm is effective for patterned fabric defect detection and an acceptable average detection rate reaches by 94.2%.

  3. Illustrating Surface Shape in Volume Data via Principal Direction-Driven 3D Line Integral Convolution

    NASA Technical Reports Server (NTRS)

    Interrante, Victoria

    1997-01-01

    The three-dimensional shape and relative depth of a smoothly curving layered transparent surface may be communicated particularly effectively when the surface is artistically enhanced with sparsely distributed opaque detail. This paper describes how the set of principal directions and principal curvatures specified by local geometric operators can be understood to define a natural 'flow' over the surface of an object, and can be used to guide the placement of the lines of a stroke texture that seeks to represent 3D shape information in a perceptually intuitive way. The driving application for this work is the visualization of layered isovalue surfaces in volume data, where the particular identity of an individual surface is not generally known a priori and observers will typically wish to view a variety of different level surfaces from the same distribution, superimposed over underlying opaque structures. By advecting an evenly distributed set of tiny opaque particles, and the empty space between them, via 3D line integral convolution through the vector field defined by the principal directions and principal curvatures of the level surfaces passing through each gridpoint of a 3D volume, it is possible to generate a single scan-converted solid stroke texture that may intuitively represent the essential shape information of any level surface in the volume. To generate longer strokes over more highly curved areas, where the directional information is both most stable and most relevant, and to simultaneously downplay the visual impact of directional information in the flatter regions, one may dynamically redefine the length of the filter kernel according to the magnitude of the maximum principal curvature of the level surface at the point around which it is applied.

  4. Shape Engineered Nanoparticle Fabrication for Biomedical Applications

    NASA Astrophysics Data System (ADS)

    Nasrullah, Azeem

    Semiconductor fabrication research has developed technologies that allow for the deposition and patterning of thin films, and can be applied to many different industries, including the field of medicine. One such application is the fabrication of nanoparticles. There is a wide variety of nanoparticle-based medical diagnostics and therapies, including drug delivery and cancer imaging. Most of the nanoparticles being studied are chemically synthesized and spherical in shape, and studies have shown that other shapes can be more useful in certain applications, especially those that involve in vivo analysis and treatment. Fabrication of particles using a tool set developed from the semiconductor industry can allow for a detailed study of size and shape dependence on nanoparticle uptake in the bloodstream. Particle fabrication is achieved using thin film deposition, ion beam proximity lithography, wet etching, and lift-off, all similar to techniques commonly found in the semiconductor industry. The particles are formed using patterns developed with proximity lithography, and this represents the largest effort in this work. An ion beam, generated by a saddle-field ion source, is used to irradiate a polymeric resist with a thin membrane stencil mask placed in close proximity to the resist coated substrate in order to define the pattern. A saddle-field ion source was constructed and characterized for proximity lithography, with a beam diameter of 4.8 mm for a +/-5% tolerance in current density, a source size range of 0.3--0.9 mm, an average brightness value of 15 nAcm2˙sr , and average exposure times of ≈30 s. Stencil masks were fabricated from silicon nitride membranes in order to generate the pattern for the nanoparticles, and the particles were fabricated using a bi-layer resist and a sacrificial copper layer for release into solution.

  5. Variations in size, shape and asymmetries of the third frontal convolution in hominids: paleoneurological implications for hominin evolution and the origin of language.

    PubMed

    Balzeau, Antoine; Gilissen, Emmanuel; Holloway, Ralph L; Prima, Sylvain; Grimaud-Hervé, Dominique

    2014-11-01

    The study of brain structural asymmetries as anatomical substrates of functional asymmetries in extant humans, great apes, and fossil hominins is of major importance in understanding the structural basis of modern human cognition. We propose methods to quantify the variation in size, shape and bilateral asymmetries of the third frontal convolution (or posterior inferior frontal gyrus) among recent modern humans, bonobos and chimpanzees, and fossil hominins using actual and virtual endocasts. These methodological improvements are necessary to extend previous qualitative studies of these features. We demonstrate both an absolute and relative bilateral increase in the size of the third frontal convolution in width and length between Pan species, as well as in hominins. We also observed a global bilateral increase in the size of the third frontal convolution across all species during hominin evolution, but also non-allometric intra-group variations independent of brain size within the fossil samples. Finally, our results show that the commonly accepted leftward asymmetry of Broca's cap is biased by qualitative observation of individual specimens. The trend during hominin evolution seems to be a reduction in size on the left compared with the right side, and also a clearer definition of the area. The third frontal convolution considered as a whole projects more laterally and antero-posteriorly in the right hemisphere. As a result, the left 'Broca's cap' looks more globular and better defined. Our results also suggest that the pattern of brain asymmetries is similar between Pan paniscus and hominins, leaving the gradient of the degree of asymmetry as the only relevant structural parameter. As the anatomical substrate related to brain asymmetry has been present since the appearance of the hominin lineage, it is not possible to prove a direct relationship between the extent of variations in the size, shape, and asymmetries of the third frontal convolution and the origin of

  6. Variations in size, shape and asymmetries of the third frontal convolution in hominids: paleoneurological implications for hominin evolution and the origin of language.

    PubMed

    Balzeau, Antoine; Gilissen, Emmanuel; Holloway, Ralph L; Prima, Sylvain; Grimaud-Hervé, Dominique

    2014-11-01

    The study of brain structural asymmetries as anatomical substrates of functional asymmetries in extant humans, great apes, and fossil hominins is of major importance in understanding the structural basis of modern human cognition. We propose methods to quantify the variation in size, shape and bilateral asymmetries of the third frontal convolution (or posterior inferior frontal gyrus) among recent modern humans, bonobos and chimpanzees, and fossil hominins using actual and virtual endocasts. These methodological improvements are necessary to extend previous qualitative studies of these features. We demonstrate both an absolute and relative bilateral increase in the size of the third frontal convolution in width and length between Pan species, as well as in hominins. We also observed a global bilateral increase in the size of the third frontal convolution across all species during hominin evolution, but also non-allometric intra-group variations independent of brain size within the fossil samples. Finally, our results show that the commonly accepted leftward asymmetry of Broca's cap is biased by qualitative observation of individual specimens. The trend during hominin evolution seems to be a reduction in size on the left compared with the right side, and also a clearer definition of the area. The third frontal convolution considered as a whole projects more laterally and antero-posteriorly in the right hemisphere. As a result, the left 'Broca's cap' looks more globular and better defined. Our results also suggest that the pattern of brain asymmetries is similar between Pan paniscus and hominins, leaving the gradient of the degree of asymmetry as the only relevant structural parameter. As the anatomical substrate related to brain asymmetry has been present since the appearance of the hominin lineage, it is not possible to prove a direct relationship between the extent of variations in the size, shape, and asymmetries of the third frontal convolution and the origin of

  7. Styrene-based shape memory foam: fabrication and mathematical modeling

    NASA Astrophysics Data System (ADS)

    Yao, Yongtao; Zhou, Tianyang; Qin, Chao; Liu, Yanju; Leng, Jinsong

    2016-10-01

    Shape memory polymer foam is a promising kind of structure in the biomedical and aerospace field. Shape memory styrene foam with uniform and controlled open-cell structure was successfully fabricated using a salt particulate leaching method. Shape recovery capability exists for foam programming in both high-temperature compression and low-temperature compression (Shape recovery properties such as shape fixing property and shape recovery ratio were also characterized. In order to provide guidance for the future fabrication of shape memory foam, the theories of Gibson and Ashby as well as differential micromechanics theory were applied to predict Young’s modulus and the mechanical behavior of SMP styrene foams during the compression process.

  8. Fabrication of trough-shaped solar collectors

    DOEpatents

    Schertz, William W.

    1978-01-01

    There is provided a radiant energy concentration and collection device formed of a one-piece thin-walled plastic substrate including a plurality of nonimaging troughs with certain metallized surfaces of the substrate serving as reflective side walls for each trough. The one-piece plastic substrate is provided with a seating surface at the bottom of each trough which conforms to the shape of an energy receiver to be seated therein.

  9. Optofluidic fabrication for 3D-shaped particles

    NASA Astrophysics Data System (ADS)

    Paulsen, Kevin S.; di Carlo, Dino; Chung, Aram J.

    2015-04-01

    Complex three-dimensional (3D)-shaped particles could play unique roles in biotechnology, structural mechanics and self-assembly. Current methods of fabricating 3D-shaped particles such as 3D printing, injection moulding or photolithography are limited because of low-resolution, low-throughput or complicated/expensive procedures. Here, we present a novel method called optofluidic fabrication for the generation of complex 3D-shaped polymer particles based on two coupled processes: inertial flow shaping and ultraviolet (UV) light polymerization. Pillars within fluidic platforms are used to deterministically deform photosensitive precursor fluid streams. The channels are then illuminated with patterned UV light to polymerize the photosensitive fluid, creating particles with multi-scale 3D geometries. The fundamental advantages of optofluidic fabrication include high-resolution, multi-scalability, dynamic tunability, simple operation and great potential for bulk fabrication with full automation. Through different combinations of pillar configurations, flow rates and UV light patterns, an infinite set of 3D-shaped particles is available, and a variety are demonstrated.

  10. Optofluidic fabrication for 3D-shaped particles

    PubMed Central

    Paulsen, Kevin S.; Di Carlo, Dino; Chung, Aram J.

    2015-01-01

    Complex three-dimensional (3D)-shaped particles could play unique roles in biotechnology, structural mechanics and self-assembly. Current methods of fabricating 3D-shaped particles such as 3D printing, injection moulding or photolithography are limited because of low-resolution, low-throughput or complicated/expensive procedures. Here, we present a novel method called optofluidic fabrication for the generation of complex 3D-shaped polymer particles based on two coupled processes: inertial flow shaping and ultraviolet (UV) light polymerization. Pillars within fluidic platforms are used to deterministically deform photosensitive precursor fluid streams. The channels are then illuminated with patterned UV light to polymerize the photosensitive fluid, creating particles with multi-scale 3D geometries. The fundamental advantages of optofluidic fabrication include high-resolution, multi-scalability, dynamic tunability, simple operation and great potential for bulk fabrication with full automation. Through different combinations of pillar configurations, flow rates and UV light patterns, an infinite set of 3D-shaped particles is available, and a variety are demonstrated. PMID:25904062

  11. Fabrication and simulation of novel crown-shaped microneedle array

    NASA Astrophysics Data System (ADS)

    Khumpuang, Sommawan; Sugiyama, Susumu

    2005-02-01

    Recently, a novel crown-shaped microneedle array fabricated by deep X-ray lithography so called, quadruplets-microneedle array was reported. The microneedle requires no hole-fabrication whilst still can be used for a blood extraction system. Due to its quadruped tip, a deep channel formed by the space between each spike is used for storing blood by a capillary force. The particular shape of the microneedle is unrealizable by other microfabrication technology apart from PCT(Plain-pattern to Cross-section Transfer) technique. Nanoscaled tips and sloped side-wall of the structure ease a smooth skin-penetration. A model for simulating the capillary height of the extracted blood for this specific shape has been developed since the typical capillary theory is suitable for the only tube shape of liquid channel. The result of simulation conforms to the practical extraction test of the microneedle. The amount of blood retained inside the microneedle can be predicted by the height obtained from the simulation. Besides the PCT technique, the electroforming of Nickel has been demonstrated in order to fabricate the mold. The injections of polycarbonate is then performed for final structures. The cost of each microneedle array after a large volume-production has been dumped to be less than a US dollar. In this paper, the fabrication process and capillary models for individual simulation of the quadruplets-microneedle will be reported.

  12. Net shape fabrication of Alpha Silicon Carbide turbine components

    NASA Technical Reports Server (NTRS)

    Storm, R. S.

    1982-01-01

    Development of Alpha Silicon Carbide components by net shape fabrication techniques has continued in conjunction with several turbine engine programs. Progress in injection molding of simple parts has been extended to much larger components. Turbine rotors fabricated by a one piece molding have been successfully spin tested above design speeds. Static components weighing up to 4.5 kg and 33 cc in diameter have also been produced using this technique. Use of sintering fixtures significantly improves dimensional control. A new Si-SiC composite material has also been developed with average strengths up to 1000 MPa (150 ksi) at 1200 C.

  13. EDITORIAL: Designer fabrication: nanotemplates get in shape Designer fabrication: nanotemplates get in shape

    NASA Astrophysics Data System (ADS)

    Demming, Anna

    2013-02-01

    People working in device design rarely see something that works without thinking how it could be made to work better. The work on anodic aluminum oxide materials in this issue provides a case in point [1]. Over the past century researchers have observed, manipulated and exploited the porous structures that result when anodizing aluminum in for example oxalic, sulfuric, and phosphoric acid solutions [1, 2]. The self-organized pore arrays have demonstrated the potential to facilitate high through-put, low-cost fabrication of nanocomposites as well as other nanostructures. The straight self-aligned nanochannels in porous anodic aluminum oxide (AAO) have long been accepted as an inherent property of these films and for many applications they are an attractive attribute. However, researchers in Taiwan have considered a novel manifestation of AAO materials which may enhance their natural attributes by generating arrays that bend [3]. Their work is an example of how even well studied systems continue to harbour surprises and scope for creative innovation. As the authors point out, 'This novel fan-out platform facilitates probing and handling many signals from different areas on a sample's surface and is therefore promising for applications in detection and manipulation at the nanoscale level'. It has long been recognized that the inter-pore distance, pore diameter and pore depth in AAO can be controlled by changing the anodization conditions. These accommodating features have motivated researchers to seek a better understanding of how to optimize fabrication conditions. A collaboration of researchers in Sweden, Chile and Uruguay studied the structural and optical properties of silver nanowires electrodeposited in commercially available nanoporous alumina templates, with a nominal pore diameter of 20 nm [4]. Their results revealed a decrease in the uniformity of pore filling with increasing deposition overpotential and suggested that overpotentials were preferred for the

  14. EDITORIAL: Designer fabrication: nanotemplates get in shape Designer fabrication: nanotemplates get in shape

    NASA Astrophysics Data System (ADS)

    Demming, Anna

    2013-02-01

    People working in device design rarely see something that works without thinking how it could be made to work better. The work on anodic aluminum oxide materials in this issue provides a case in point [1]. Over the past century researchers have observed, manipulated and exploited the porous structures that result when anodizing aluminum in for example oxalic, sulfuric, and phosphoric acid solutions [1, 2]. The self-organized pore arrays have demonstrated the potential to facilitate high through-put, low-cost fabrication of nanocomposites as well as other nanostructures. The straight self-aligned nanochannels in porous anodic aluminum oxide (AAO) have long been accepted as an inherent property of these films and for many applications they are an attractive attribute. However, researchers in Taiwan have considered a novel manifestation of AAO materials which may enhance their natural attributes by generating arrays that bend [3]. Their work is an example of how even well studied systems continue to harbour surprises and scope for creative innovation. As the authors point out, 'This novel fan-out platform facilitates probing and handling many signals from different areas on a sample's surface and is therefore promising for applications in detection and manipulation at the nanoscale level'. It has long been recognized that the inter-pore distance, pore diameter and pore depth in AAO can be controlled by changing the anodization conditions. These accommodating features have motivated researchers to seek a better understanding of how to optimize fabrication conditions. A collaboration of researchers in Sweden, Chile and Uruguay studied the structural and optical properties of silver nanowires electrodeposited in commercially available nanoporous alumina templates, with a nominal pore diameter of 20 nm [4]. Their results revealed a decrease in the uniformity of pore filling with increasing deposition overpotential and suggested that overpotentials were preferred for the

  15. Fabrication of magnetic shape memory alloy/polymer composites

    NASA Astrophysics Data System (ADS)

    Ham-Su, R.; Healey, J. P.; Underhill, R. S.; Farrell, S. P.; Cheng, L. M.; Hyatt, C. V.; Rogge, R.; Gharghouri, M. A.

    2005-05-01

    NiMnGa-based magnetic shape memory (MSM) alloys have attained magnetic-field-induced strains up to approximately 10%, making them very attractive for a variety of applications. However, for applications that require the use of an alternating magnetic field, eddy current losses can be significant. Also, NiMnGa-based MSM alloys' fracture toughness is relatively low. Using these materials in the form of particles embedded in a polymer matrix composite could mitigate these limitations. Since the MSM effect is anisotropic, the crystallographic texture of the particles in the composites is of great interest. In this work, a procedure for fabricating NiMnGa-based MSMA/elastomer composites is described. Processing routes for optimizing the crystallographic texture in the composites are considered.

  16. Thermocapillary Technique for Shaping and Fabricating Optical Ribbon Waveguides

    NASA Astrophysics Data System (ADS)

    Fiedler, Kevin; Troian, Sandra

    The demand for ever increasing bandwidth and higher speed communication has ushered the next generation optoelectronic integrated circuits which directly incorporate polymer optical waveguide devices. Polymer melts are very versatile materials which have been successfully cast into planar single- and multimode waveguides using techniques such as embossing, photolithography and direct laser writing. In this talk, we describe a novel thermocapillary patterning method for fabricating waveguides in which the free surface of an ultrathin molten polymer film is exposed to a spatially inhomogeneous temperature field via thermal conduction from a nearby cooled mask pattern held in close proximity. The ensuring surface temperature distribution is purposely designed to pool liquid selectively into ribbon shapes suitable for optical waveguiding, but with rounded and not rectangular cross sectional areas due to capillary forces. The solidified waveguide patterns which result from this non-contact one step procedure exhibit ultrasmooth interfaces suitable for demanding optoelectronic applications. To complement these studies, we have also conducted finite element simulations for quantifying the influence of non-rectangular cross-sectional shapes on mode propagation and losses. Kf gratefully acknowledges support from a NASA Space Technology Research Fellowship.

  17. Fabrication of ceramic components using mold shape deposition manufacturing

    NASA Astrophysics Data System (ADS)

    Cooper, Alexander G.

    Mold Shape Deposition Manufacturing (Mold SDM) is a new process for the fabrication of geometrically complex, structural ceramic components. This thesis describes the development of the Mold SDM process, including process steps, materials selection, planning strategies and automation. Initial characterization results are presented and these are used to compare the process to competing manufacturing processes. A range of current and potential applications for ceramic, as well as metal and polymer parts are discussed. The benefits and limitations of ceramic materials for structural applications are discussed to motivate the need for a manufacturing process capable of rapidly producing high quality, geometrically complex, structural ceramic components. The Mold SDM process was developed to address this need. Mold SDM is based on Shape Deposition Manufacturing (SDM) and uses SDM techniques to build fugitive wax molds which can then be used to build ceramic parts by gelcasting. SDM is an additive-subtractive layered manufacturing process which allows it to build geometrically complex parts. The subtraction step differentiates Mold SDM from other layered manufacturing processes and allows accurate, high quality surfaces to be produced. The performance of the process was increased by identifying the key material properties and then selecting improved materials combinations. Candidate materials were evaluated in terms of machinability, shrinkage, heat resistance and chemical compatibility. A number of preferred materials combinations were developed and used to produce ceramic, metal and polymer parts. A number of new process planning strategies and build techniques were developed. The manufacturability analysis determines whether a part is manufacturable and the orientation selection guidelines help in the selection of optimum build directions. New decomposition techniques take advantage of process capabilities to improve part quality and build rate. Initial process

  18. Fabrication of Adhesive Lenses Using Free Surface Shaping

    NASA Astrophysics Data System (ADS)

    Hoheisel, D.; Kelb, C.; Wall, M.; Roth, B.; Rissing, L.

    2013-09-01

    Two approaches for fabricating polymer lenses are presented in this paper. Both are based on filling circular holes with UV curing adhesives. Initially, the viscous adhesive material creates a liquid and spherical free surface due to its own surface tension. This shape is then preserved by curing with UV-hardening light. For the first approach, the holes are generated in a 4 inch Si-wafer by deep reactive ion etching (DRIE) and for the second, a polydimethylsiloxane (PDMS) mould is manufactured. Three types of UV-curing adhesives are investigated (NOA 61, NOA 88 and NEA 121 by Norland Products). Preliminary to the determination of the lens curvature, a contact angle goniometer is used for taking side view images of the lenses. The radius of curvature is then extracted via image processing with the software MATLAB®. Furthermore, the surface roughness of the PDMS mould and the generated lenses is measured with a white light interferometer to characterize the casting process. The resolution power of the generated lenses is evaluated by measurement of their point spread functions (psf) and modulation transfer functions (mtf), respectively.

  19. Distal Convoluted Tubule

    PubMed Central

    Ellison, David H.

    2014-01-01

    The distal convoluted tubule is the nephron segment that lies immediately downstream of the macula densa. Although short in length, the distal convoluted tubule plays a critical role in sodium, potassium, and divalent cation homeostasis. Recent genetic and physiologic studies have greatly expanded our understanding of how the distal convoluted tubule regulates these processes at the molecular level. This article provides an update on the distal convoluted tubule, highlighting concepts and pathophysiology relevant to clinical practice. PMID:24855283

  20. Method for fabricating uranium alloy articles without shape memory effects

    DOEpatents

    Banker, John G.

    1985-01-01

    Uranium-rich niobium and niobium-zirconium alloys possess a characteristic known as shape memory effect wherein shaped articles of these alloys recover their original shape when heated. The present invention circumvents this memory behavior by forming the alloys into the desired configuration at elevated temperatures with "cold" matched dies and maintaining the shaped articles between the dies until the articles cool to ambient temperature.

  1. Method for fabricating uranium alloy articles without shape memory effects

    DOEpatents

    Banker, J.G.

    1980-05-21

    Uranium-rich niobium and niobium-zirconium alloys possess a characteristic known as shape memory effect wherein shaped articles of these alloys recover their original shape when heated. The present invention circumvents this memory behavior by forming the alloys into the desired configuration at elevated temperatures with cold matched dies and maintaining the shaped articles between the dies until the articles cool to ambient temperature.

  2. Fabrication of porous NiTi shape memory alloy structures using laser engineered net shaping.

    PubMed

    Krishna, B Vamsi; Bose, Susmita; Bandyopadhyay, Amit

    2009-05-01

    Porous NiTi alloy samples were fabricated with 12-36% porosity from equiatomic NiTi alloy powder using laser engineered net shaping (LENS). The effects of processing parameters on density and properties of laser-processed NiTi alloy samples were investigated. It was found that the density increased rapidly with increasing the specific energy input up to 50 J/mm(3). Further increase in the energy input had small effect on density. High cooling rates associated with LENS processing resulted in higher amount of cubic B2 phase, and increased the reverse transformation temperatures of porous NiTi samples due to thermally induced stresses and defects. Transformation temperatures were found to be independent of pore volume, though higher pore volume in the samples decreased the maximum recoverable strain from 6% to 4%. Porous NiTi alloy samples with 12-36% porosity exhibited low Young's modulus between 2 and 18 GPa as well as high compressive strength and recoverable strain. Because of high open pore volume between 36% and 62% of total volume fraction porosity, these porous NiTi alloy samples can potentially accelerate the healing process and improve biological fixation when implanted in vivo. Thus porous NiTi is a promising biomaterial for hard tissue replacements.

  3. Method for fabrication of cylindrical microlenses of selected shape

    DOEpatents

    Snyder, James J.; Baer, Thomas M.

    1992-01-01

    The present invention provides a diffraction limited, high numerical aperture (fast) cylindrical microlens. The method for making the microlens is adaptable to produce a cylindrical lens that has almost any shape on its optical surfaces. The cylindrical lens may have a shape, such as elliptical or hyperbolic, designed to transform some particular given input light distribution into some desired output light distribution. In the method, the desired shape is first formed in a glass preform. Then, the preform is heated to the minimum drawing temperature and a fiber is drawn from it. The cross-sectional shape of the fiber bears a direct relation to the shape of the preform from which it was drawn. During the drawing process, the surfaces become optically smooth due to fire polishing. The present invention has many applications, such as integrated optics, optical detectors and laser diodes. The lens, when connected to a laser diode bar, can provide a high intensity source of laser radiation for pumping a high average power solid state laser. In integrated optics, a lens can be used to couple light into and out of apertures such as waveguides. The lens can also be used to collect light, and focus it on a detector.

  4. Method for fabrication of cylindrical microlenses of selected shape

    DOEpatents

    Snyder, J.J.; Baer, T.M.

    1992-01-14

    The present invention provides a diffraction limited, high numerical aperture (fast) cylindrical microlens. The method for making the microlens is adaptable to produce a cylindrical lens that has almost any shape on its optical surfaces. The cylindrical lens may have a shape, such as elliptical or hyperbolic, designed to transform some particular given input light distribution into some desired output light distribution. In the method, the desired shape is first formed in a glass preform. Then, the preform is heated to the minimum drawing temperature and a fiber is drawn from it. The cross-sectional shape of the fiber bears a direct relation to the shape of the preform from which it was drawn. During the drawing process, the surfaces become optically smooth due to fire polishing. The present invention has many applications, such as integrated optics, optical detectors and laser diodes. The lens, when connected to a laser diode bar, can provide a high intensity source of laser radiation for pumping a high average power solid state laser. In integrated optics, a lens can be used to couple light into and out of apertures such as waveguides. The lens can also be used to collect light, and focus it on a detector. 11 figs.

  5. Progress in net shape fabrication of alpha sic turbine components

    NASA Technical Reports Server (NTRS)

    Sweeting, T. B.; Frechette, F. J.; Macbeth, J. W.

    1984-01-01

    An update of the status of ceramic component development of the AGT Programs is presented. Activity on AGTO Program focussed on the following: successful transition from the prototype to engine configuration rotor, investigation of alternate rotor molding techniques, and completion of scroll assemblies. Progress on the Garrett AGT Program was highlighted by the introduction of plastic molding and extrusion to parts which were previously fabricated by slip casting and isopressing respectively.

  6. Near net shape processing for solar thermal propulsion hardware using directed light fabrication

    SciTech Connect

    Milewski, J.O.; Fonseca, J.C.; Lewis, G.K.

    1998-12-01

    Directed light fabrication (DLF) is a direct metal deposition process that fuses gas delivered powder, in the focal zone of a high powered laser beam to form fully fused near net shaped components. The near net shape processing of rhenium, tungsten, iridium and other high temperature materials may offer significant cost savings compared with conventional processing. This paper describes a 3D parametric solid model, integrated with a manufacturing model, and creating a control field which runs on the DLF machine directly depositing a fully dense, solid metal, near net shaped, nozzle component. Examples of DLF deposited rhenium, iridium and tantalum, from previous work, show a continuously solidified microstructure in rod and tube shapes. Entrapped porosity indicates the required direction for continued process development. These combined results demonstrate the potential for a new method to fabricate complex near net shaped components using materials of interest to the space and aerospace industries.

  7. Scalable, Shape-specific, Top-down Fabrication Methods for the Synthesis of Engineered Colloidal Particles

    PubMed Central

    Merkel, Timothy J.; Herlihy, Kevin P.; Nunes, Janine; Orgel, Ryan M.; Rolland, Jason P.; DeSimone, Joseph M.

    2010-01-01

    The search for a method to fabricate non-spherical colloidal particles from a variety of materials is of growing interest. As the commercialization of nanotechnology continues to expand, the ability to translate particle fabrication methods from a laboratory to an industrial scale is of increasing significance. In this article, we examine several of the most readily scalable top-down methods for the fabrication of such shape specific particles and compare their capabilities with respect to particle composition, size, shape and complexity as well as the scalability of the method. We offer an extensive examination of Particle Replication In Non-wetting Templates (PRINT®) with regards to the versatility and scalability of this technique. We also detail the specific methods used in PRINT particle fabrication, including harvesting, purification and surface modification techniques, with examination of both past and current methods. PMID:20000620

  8. Fabrication and Characterization of Carbon Nanofiber Reinforced Shape Memory Epoxy (CNFR-SME) Composites

    NASA Astrophysics Data System (ADS)

    Wang, Jiuyang

    Shape memory polymers have a wide range of applications due to their ability to mechanically change shapes upon external stimulus, while their achievable composite counterparts prove even more versatile. An overview of literature on shape memory materials, fillers and composites was provided to pave a foundation for the materials used in the current study and their inherent benefits. This study details carbon nanofiber and composite fabrication and contrasts their material properties. In the first section, the morphology and surface chemistry of electrospun-poly(acrylonitrile)-based carbon nanofiber webs were tailored through various fabrication methods and impregnated with a shape memory epoxy. The morphologies, chemical compositions, thermal stabilities and electrical resistivities of the carbon nanofibers and composites were then characterized. In the second section, an overview of thermal, mechanical and shape memory characterization techniques for shape memory polymers and their composites was provided. Thermal and mechanical properties in addition to the kinetic and dynamic shape memory performances of neat epoxy and carbon nanofiber/epoxy composites were characterized. The various carbon nanofiber web modifications proved to have notable influence on their respective composite performances. The results from these two sections lead to an enhanced understanding of these carbon nanofiber reinforced shape memory epoxy composites and provided insight for future studies to tune these composites at will.

  9. Fabrication and Testing of a Leading-Edge-Shaped Heat Pipe

    NASA Technical Reports Server (NTRS)

    Glass, David E.; Merrigan, Michael A.; Sena, J. Tom; Reid, Robert S.

    1998-01-01

    The development of a refractory-composite/heat-pipe-cooled leading edge has evolved from the design stage to the fabrication and testing of a full size, leading-edge-shaped heat pipe. The heat pipe had a 'D-shaped' cross section and was fabricated from arc cast Mo-4lRe. An artery was included in the wick. Several issues were resolved with the fabrication of the sharp leading edge radius heat pipe. The heat pipe was tested in a vacuum chamber at Los Alamos National Laboratory using induction heating and was started up from the frozen state several times. However, design temperatures and heat fluxes were not obtained due to premature failure of the heat pipe resulting from electrical discharge between the induction heating apparatus and the heat pipe. Though a testing anomaly caused premature failure of the heat pipe, successful startup and operation of the heat pipe was demonstrated.

  10. Rapid fabrication of cylindrical microlens array by shaped femtosecond laser direct writing

    NASA Astrophysics Data System (ADS)

    Luo, Zhi; Wang, Cong; Yin, Kai; Dong, Xinran; Chu, Dongkai; Duan, Ji'an

    2016-07-01

    In this study, a remarkable spatial shaping approach is proposed to transform Gaussian femtosecond laser into quasi-Bessel optical field with compressed central lobe and amplified side lobes of the spatial intensity profile. Based on this technique, inward bulge trench (IBT) structures are fabricated with high efficiency on the surface of PMMA by a single illumination step, whose cross-sectional profile is opposite to the results fabricated by Gaussian beam. And plano-convex cylindrical microlens array, which is consistent in size and shape throughout a large sample area, is formed through simply piecing together the IBT structures during fabricating process. Furthermore, numerical simulations of optical field in radial direction and on-axial direction are exploited to rationalize the dependence of the patterned microstructures on the spatial intensity distribution of femtosecond laser.

  11. Scalable shape-controlled fabrication of curved microstructures using a femtosecond laser wet-etching process.

    PubMed

    Bian, Hao; Yang, Qing; Chen, Feng; Liu, Hewei; Du, Guangqing; Deng, Zefang; Si, Jinhai; Yun, Feng; Hou, Xun

    2013-07-01

    Materials with curvilinear surface microstructures are highly desirable for micro-optical and biomedical devices. However, realization of such devices efficiently remains technically challenging. This paper demonstrates a facile and flexible method to fabricate curvilinear microstructures with controllable shapes and dimensions. The method composes of femtosecond laser exposures and chemical etching process with the hydrofluoric acid solutions. By fixed-point and step-in laser irradiations followed by the chemical treatments, concave microstructures with different profiles such as spherical, conical, bell-like and parabola were fabricated on silica glasses. The convex structures were replicated on polymers by the casting replication process. In this work, we used this technique to fabricate high-quality microlens arrays and high-aspect-ratio microwells which can be used in 3D cell culture. This approach offers several advantages such as high-efficient, scalable shape-controllable and easy manipulations.

  12. Free form fabrication of metallic components using laser engineered net shaping (LENS{trademark})

    SciTech Connect

    Griffith, M.L.; Keicher, D.M.; Atwood, C.L.

    1996-09-01

    Solid free form fabrication is one of the fastest growing automated manufacturing technologies that has significantly impacted the length of time between initial concept and actual part fabrication. Starting with CAD renditions of new components, several techniques such as stereolithography and selective laser sintering are being used to fabricate highly accurate complex three-dimensional concept models using polymeric materials. Coupled with investment casting techniques, sacrificial polymeric objects are used to minimize costs and time to fabricate tooling used to make complex metal castings. This paper will describe recent developments in a new technology, known as LENS{sup {trademark}} (Laser Engineered Net Shaping), to fabricate metal components directly from CAD solid models and thus further reduce the lead times for metal part fabrication. In a manner analogous to stereolithography or selective sintering, the LENS{sup {trademark}} process builds metal parts line by line and layer by layer. Metal particles are injected into a laser beam, where they are melted and deposited onto a substrate as a miniature weld pool. The trace of the laser beam on the substrate is driven by the definition of CAD models until the desired net-shaped densified metal component is produced.

  13. Laser Spray Fabrication for Net-Shape Rapid Product Realization LDRD

    SciTech Connect

    Atwood, C.L.; Ensz, M.T.; Greene, D.L.; Griffith, M.L.; Harwell, L.D.; Jeantette, F.P.; Keicher, D.M.; Oliver, M.S.; Reckaway, D.E.; Romero, J.A.; Schlienger, M.E.; Smugeresky, J.D.

    1999-04-01

    The primary purpose of this LDRD project was to characterize the laser deposition process and determine the feasibility of fabricating complex near-net shapes directly from a CAD solid model. Process characterization provided direction in developing a system to fabricate complex shapes directly from a CAD solid model. Our goal for this LDRD was to develop a system that is robust and provides a significant advancement to existing technologies (e.g., polymeric-based rapid prototyping, laser welding). Development of the process will allow design engineers to produce functional models of their designs directly from CAD files. The turnaround time for complex geometrical shaped parts will be hours instead of days and days instead of months. With reduced turnaround time, more time can be spent on the product-design phase to ensure that the best component design is achieved. Maturation of this technology will revolutionize the way the world produces structural components.

  14. Fabrication method for cores of structural sandwich materials including star shaped core cells

    DOEpatents

    Christensen, Richard M.

    1997-01-01

    A method for fabricating structural sandwich materials having a core pattern which utilizes star and non-star shaped cells. The sheets of material are bonded together or a single folded sheet is used, and bonded or welded at specific locations, into a flat configuration, and are then mechanically pulled or expanded normal to the plane of the sheets which expand to form the cells. This method can be utilized to fabricate other geometric cell arrangements than the star/non-star shaped cells. Four sheets of material (either a pair of bonded sheets or a single folded sheet) are bonded so as to define an area therebetween, which forms the star shaped cell when expanded.

  15. Fabrication method for cores of structural sandwich materials including star shaped core cells

    DOEpatents

    Christensen, R.M.

    1997-07-15

    A method for fabricating structural sandwich materials having a core pattern which utilizes star and non-star shaped cells is disclosed. The sheets of material are bonded together or a single folded sheet is used, and bonded or welded at specific locations, into a flat configuration, and are then mechanically pulled or expanded normal to the plane of the sheets which expand to form the cells. This method can be utilized to fabricate other geometric cell arrangements than the star/non-star shaped cells. Four sheets of material (either a pair of bonded sheets or a single folded sheet) are bonded so as to define an area therebetween, which forms the star shaped cell when expanded. 3 figs.

  16. Powder-Coated Towpreg: Avenues to Near Net Shape Fabrication of High Performance Composites

    NASA Technical Reports Server (NTRS)

    Johnston, N. J.; Cano, R. J.; Marchello, J. M.; Sandusky, D. A.

    1995-01-01

    Near net shape parts were fabricated from powder-coated preforms. Key issues including powder loss during weaving and tow/tow friction during braiding were addressed, respectively, by fusing the powder to the fiber prior to weaving and applying a water-based gel to the towpreg prior to braiding. A 4:1 debulking of a complex 3-D woven powder-coated preform was achieved in a single step utilizing expansion rubber molding. Also, a process was developed for using powder-coated towpreg to fabricate consolidated ribbon having good dimensional integrity and low voids. Such ribbon will be required for in situ fabrication of structural components via heated head advanced tow placement. To implement process control and ensure high quality ribbon, the ribbonizer heat transfer and pulling force were modeled from fundamental principles. Most of the new ribbons were fabricated from dry polyarylene ether and polymide powders.

  17. Laser Engineered Net Shaping (LENS(TM)): A Tool for Direct Fabrication of Metal Parts

    SciTech Connect

    Atwood, C.; Ensz, M.; Greene, D.; Griffith, M.; Harwell, L.; Reckaway, D.; Romero, T.; Schlienger, E.; Smugeresky, J.

    1998-11-05

    For many years, Sandia National Laboratories has been involved in the development and application of rapid prototyping and dmect fabrication technologies to build prototype parts and patterns for investment casting. Sandia is currently developing a process called Laser Engineered Net Shaping (LENS~) to fabricate filly dense metal parts dwectly from computer-aided design (CAD) solid models. The process is similar to traditional laser-initiated rapid prototyping technologies such as stereolithography and selective laser sintering in that layer additive techniques are used to fabricate physical parts directly from CAD data. By using the coordinated delivery of metal particles into a focused laser beam apart is generated. The laser beam creates a molten pool of metal on a substrate into which powder is injected. Concurrently, the substrate on which the deposition is occurring is moved under the beam/powder interaction zone to fabricate the desired cross-sectiwal geometry. Consecutive layers are additively deposited, thereby producing a three-dmensional part. This process exhibits enormous potential to revolutionize the way in which metal parts, such as complex prototypes, tooling, and small-lot production parts, are produced. The result is a comple~ filly dense, near-net-shape part. Parts have been fabricated from 316 stainless steel, nickel-based alloys, H13 tool steel, and titanium. This talk will provide a general overview of the LENS~ process, discuss potential applications, and display as-processed examples of parts.

  18. Laser engineered net shaping (LENS) for the fabrication of metallic components

    SciTech Connect

    Griffith, M.L.; Keicher, D.L.; Romero, J.A.; Atwood, C.L.; Harwell, L.D.; Greene, D.L.; Smugeresky, J.E.

    1996-06-01

    Solid free form fabrication is a fast growing automated manufacturing technology that has reduced the time between initial concept and fabrication. Starting with CAD renditions of new components, techniques such as stereolithography and selective laser sintering are being used to fabricate highly accurate complex 3-D objects using polymers. Together with investment casting, sacrificial polymeric objects are used to minimize cost and time to fabricate tooling used to make complex metal casting. This paper describes recent developments in LENS{trademark} (Laser Engineered Net Shaping) to fabricate the metal components {ital directly} from CAD solid models and thus further reduce the lead time. Like stereolithography or selective sintering, LENS builds metal parts line by line and layer by layer. Metal particles are injected into a laser beam where they are melted and deposited onto a substrate as a miniature weld pool. The trace of the laser beam on the substrate is driven by the definition of CAD models until the desired net-shaped densified metal component is produced.

  19. Asymmetric quantum convolutional codes

    NASA Astrophysics Data System (ADS)

    La Guardia, Giuliano G.

    2016-01-01

    In this paper, we construct the first families of asymmetric quantum convolutional codes (AQCCs). These new AQCCs are constructed by means of the CSS-type construction applied to suitable families of classical convolutional codes, which are also constructed here. The new codes have non-catastrophic generator matrices, and they have great asymmetry. Since our constructions are performed algebraically, i.e. we develop general algebraic methods and properties to perform the constructions, it is possible to derive several families of such codes and not only codes with specific parameters. Additionally, several different types of such codes are obtained.

  20. Tunable Diffractive Optical Elements Based on Shape-Memory Polymers Fabricated via Hot Embossing.

    PubMed

    Schauer, Senta; Meier, Tobias; Reinhard, Maximilian; Röhrig, Michael; Schneider, Marc; Heilig, Markus; Kolew, Alexander; Worgull, Matthias; Hölscher, Hendrik

    2016-04-13

    We introduce actively tunable diffractive optical elements fabricated from shape-memory polymers (SMPs). By utilizing the shape-memory effect of the polymer, at least one crucial attribute of the diffractive optical element (DOE) is tunable and adjustable subsequent to the completed fabrication process. A thermoplastic, transparent, thermoresponsive polyurethane SMP was structured with diverse diffractive microstructures via hot embossing. The tunability was enabled by programming a second, temporary shape into the diffractive optical element by mechanical deformation, either by stretching or a second embossing cycle at low temperatures. Upon exposure to the stimulus heat, the structures change continuously and controllable in a predefined way. We establish the novel concept of shape-memory diffractive optical elements by illustrating their capabilities, with regard to tunability, by displaying the morphing diffractive pattern of a height tunable and a period tunable structure, respectively. A sample where an arbitrary structure is transformed to a second, disparate one is illustrated as well. To prove the applicability of our tunable shape-memory diffractive optical elements, we verified their long-term stability and demonstrated the precise adjustability with a detailed analysis of the recovery dynamics, in terms of temperature dependence and spatially resolved, time-dependent recovery. PMID:26998646

  1. Tunable Diffractive Optical Elements Based on Shape-Memory Polymers Fabricated via Hot Embossing.

    PubMed

    Schauer, Senta; Meier, Tobias; Reinhard, Maximilian; Röhrig, Michael; Schneider, Marc; Heilig, Markus; Kolew, Alexander; Worgull, Matthias; Hölscher, Hendrik

    2016-04-13

    We introduce actively tunable diffractive optical elements fabricated from shape-memory polymers (SMPs). By utilizing the shape-memory effect of the polymer, at least one crucial attribute of the diffractive optical element (DOE) is tunable and adjustable subsequent to the completed fabrication process. A thermoplastic, transparent, thermoresponsive polyurethane SMP was structured with diverse diffractive microstructures via hot embossing. The tunability was enabled by programming a second, temporary shape into the diffractive optical element by mechanical deformation, either by stretching or a second embossing cycle at low temperatures. Upon exposure to the stimulus heat, the structures change continuously and controllable in a predefined way. We establish the novel concept of shape-memory diffractive optical elements by illustrating their capabilities, with regard to tunability, by displaying the morphing diffractive pattern of a height tunable and a period tunable structure, respectively. A sample where an arbitrary structure is transformed to a second, disparate one is illustrated as well. To prove the applicability of our tunable shape-memory diffractive optical elements, we verified their long-term stability and demonstrated the precise adjustability with a detailed analysis of the recovery dynamics, in terms of temperature dependence and spatially resolved, time-dependent recovery.

  2. Laundering durable antibacterial cotton fabrics grafted with pomegranate-shaped polymer wrapped in silver nanoparticle aggregations

    NASA Astrophysics Data System (ADS)

    Liu, Hanzhou; Lv, Ming; Deng, Bo; Li, Jingye; Yu, Ming; Huang, Qing; Fan, Chunhai

    2014-08-01

    To improve the laundering durability of the silver functionalized antibacterial cotton fabrics, a radiation-induced coincident reduction and graft polymerization is reported herein where a pomegranate-shaped silver nanoparticle aggregations up to 500 nm can be formed due to the coordination forces between amino group and silver and the wrapping procedure originated from the coincident growth of the silver nanoparticles and polymer graft chains. This pomegranate-shaped silver NPAs functionalized cotton fabric exhibits outstanding antibacterial activities and also excellent laundering durability, where it can inactivate higher than 90% of both E. coli and S. aureus even after 50 accelerated laundering cycles, which is equivalent to 250 commercial or domestic laundering cycles.

  3. Laundering durable antibacterial cotton fabrics grafted with pomegranate-shaped polymer wrapped in silver nanoparticle aggregations.

    PubMed

    Liu, Hanzhou; Lv, Ming; Deng, Bo; Li, Jingye; Yu, Ming; Huang, Qing; Fan, Chunhai

    2014-08-01

    To improve the laundering durability of the silver functionalized antibacterial cotton fabrics, a radiation-induced coincident reduction and graft polymerization is reported herein where a pomegranate-shaped silver nanoparticle aggregations up to 500 nm can be formed due to the coordination forces between amino group and silver and the wrapping procedure originated from the coincident growth of the silver nanoparticles and polymer graft chains. This pomegranate-shaped silver NPAs functionalized cotton fabric exhibits outstanding antibacterial activities and also excellent laundering durability, where it can inactivate higher than 90% of both E. coli and S. aureus even after 50 accelerated laundering cycles, which is equivalent to 250 commercial or domestic laundering cycles.

  4. Laundering durable antibacterial cotton fabrics grafted with pomegranate-shaped polymer wrapped in silver nanoparticle aggregations

    PubMed Central

    Liu, Hanzhou; Lv, Ming; Deng, Bo; Li, Jingye; Yu, Ming; Huang, Qing; Fan, Chunhai

    2014-01-01

    To improve the laundering durability of the silver functionalized antibacterial cotton fabrics, a radiation-induced coincident reduction and graft polymerization is reported herein where a pomegranate-shaped silver nanoparticle aggregations up to 500 nm can be formed due to the coordination forces between amino group and silver and the wrapping procedure originated from the coincident growth of the silver nanoparticles and polymer graft chains. This pomegranate-shaped silver NPAs functionalized cotton fabric exhibits outstanding antibacterial activities and also excellent laundering durability, where it can inactivate higher than 90% of both E. coli and S. aureus even after 50 accelerated laundering cycles, which is equivalent to 250 commercial or domestic laundering cycles. PMID:25082297

  5. Fabrication of cone-shaped boron doped diamond and gold nanoelectrodes for AFM-SECM

    NASA Astrophysics Data System (ADS)

    Avdic, A.; Lugstein, A.; Wu, M.; Gollas, B.; Pobelov, I.; Wandlowski, T.; Leonhardt, K.; Denuault, G.; Bertagnolli, E.

    2011-04-01

    We demonstrate a reliable microfabrication process for a combined atomic force microscopy (AFM) and scanning electrochemical microscopy (SECM) measurement tool. Integrated cone-shaped sensors with boron doped diamond (BDD) or gold (Au) electrodes were fabricated from commercially available AFM probes. The sensor formation process is based on mature semiconductor processing techniques, including focused ion beam (FIB) machining, and highly selective reactive ion etching (RIE). The fabrication approach preserves the geometry of the original AFM tips resulting in well reproducible nanoscaled sensors. The feasibility and functionality of the fully featured tips are demonstrated by cyclic voltammetry, showing good agreement between the measured and calculated currents of the cone-shaped AFM-SECM electrodes.

  6. Fabrication of cone-shaped boron doped diamond and gold nanoelectrodes for AFM-SECM.

    PubMed

    Avdic, A; Lugstein, A; Wu, M; Gollas, B; Pobelov, I; Wandlowski, T; Leonhardt, K; Denuault, G; Bertagnolli, E

    2011-04-01

    We demonstrate a reliable microfabrication process for a combined atomic force microscopy (AFM) and scanning electrochemical microscopy (SECM) measurement tool. Integrated cone-shaped sensors with boron doped diamond (BDD) or gold (Au) electrodes were fabricated from commercially available AFM probes. The sensor formation process is based on mature semiconductor processing techniques, including focused ion beam (FIB) machining, and highly selective reactive ion etching (RIE). The fabrication approach preserves the geometry of the original AFM tips resulting in well reproducible nanoscaled sensors. The feasibility and functionality of the fully featured tips are demonstrated by cyclic voltammetry, showing good agreement between the measured and calculated currents of the cone-shaped AFM-SECM electrodes.

  7. Fabrication of cone-shaped boron doped diamond and gold nanoelectrodes for AFM-SECM.

    PubMed

    Avdic, A; Lugstein, A; Wu, M; Gollas, B; Pobelov, I; Wandlowski, T; Leonhardt, K; Denuault, G; Bertagnolli, E

    2011-04-01

    We demonstrate a reliable microfabrication process for a combined atomic force microscopy (AFM) and scanning electrochemical microscopy (SECM) measurement tool. Integrated cone-shaped sensors with boron doped diamond (BDD) or gold (Au) electrodes were fabricated from commercially available AFM probes. The sensor formation process is based on mature semiconductor processing techniques, including focused ion beam (FIB) machining, and highly selective reactive ion etching (RIE). The fabrication approach preserves the geometry of the original AFM tips resulting in well reproducible nanoscaled sensors. The feasibility and functionality of the fully featured tips are demonstrated by cyclic voltammetry, showing good agreement between the measured and calculated currents of the cone-shaped AFM-SECM electrodes. PMID:21368355

  8. Design and fabrication of hat-shaped stiffened panel by resin transfer molding method

    SciTech Connect

    Bell, R.; Ibekwe, S.I.; Mensah, P.F.; Chehl, S.S.

    1998-12-31

    Hat-shaped stiffened composite panels were fabricated by resin transfer molding (RTM) process. As application of these compression load bearing panels in aircraft wings and fuselage increases, this promising manufacturing technique would contribute towards the goal of attaining reduced part counts and cheaper manufacturing costs. Rigid foam which increases the structural efficiency of panels was utilized in this process. Also Balsa wood was considered as an alternative to the rigid foam and employed as a permanent mandrel in fabricating one of the panels. Buckling analysis result by finite element method and modified closed form solution suggested by Agarwal et al. (1974) agree. Once this process is fine-tuned, it would provide a cheaper method of fabricating composite hat-stiffened panels.

  9. Development of a method for fabricating metallic matrix composite shapes by a continuous mechanical process

    NASA Technical Reports Server (NTRS)

    Divecha, A. P.

    1974-01-01

    Attempts made to develop processes capable of producing metal composites in structural shapes and sizes suitable for space applications are described. The processes must be continuous and promise to lower fabrication costs. Special attention was given to the aluminum boride (Al/b) composite system. Results show that despite adequate temperature control, the consolidation characteristics did not improve as expected. Inadequate binder removal was identified as the cause responsible. An Al/c (aluminum-graphite) composite was also examined.

  10. Progress in net shape fabrication of alpha SiC turbine components

    NASA Technical Reports Server (NTRS)

    Storm, R. S.; Naum, R. G.

    1983-01-01

    The development status of component technology in an automotive gas turbine Ceramic Applications in Turbine Engines program is discussed, with attention to such materials and processes having a low cost, net shape fabrication potential as sintered alpha-SiC that has been fashioned by means of injection molding, slip casting, and isostatic pressing. The gas turbine elements produced include a gasifier turbine rotor, a turbine wheel, a connecting duct, a combustor baffle, and a transition duct.

  11. Fine-tuned grayscale optofluidic maskless lithography for three-dimensional freeform shape microstructure fabrication.

    PubMed

    Song, Suk-Heung; Kim, Kibeom; Choi, Sung-Eun; Han, Sangkwon; Lee, Ho-Suk; Kwon, Sunghoon; Park, Wook

    2014-09-01

    This article presents free-floating three-dimensional (3D) microstructure fabrication in a microfluidic channel using direct fine-tuned grayscale image lithography. The image is designed as a freeform shape and is composed of gray shades as light-absorbing features. Gray shade levels are modulated through multiple reflections of light in a digital micromirror device (DMD) to produce different height formations. Whereas conventional photolithography has several limitations in producing grayscale colors on photomask features, our method focuses on a maskless, single-shot process for fabrication of freeform 3D micro-scale shapes. The fine-tuned gray image is designed using an 8-bit grayscale color; thus, each pixel is capable of displaying 256 gray shades. The pattern of the UV light reflecting on the DMD is transferred to a photocurable resin flowing through a microfluidic channel. Here, we demonstrate diverse free-floating 3D microstructure fabrication using fine-tuned grayscale image lithography. Additionally, we produce polymeric microstructures with locally embedded gray encoding patterns, such as grayscale-encoded microtags. This functional microstructure can be applied to a biophysical detection system combined with 3D microstructures. This method would be suitable for fabricating 3D microstructures that have a specific morphology to be used for particular biological or medical applications.

  12. Simple and Reliable Fabrication of Bioinspired Mushroom-Shaped Micropillars with Precisely Controlled Tip Geometries.

    PubMed

    Yi, Hoon; Kang, Minsu; Kwak, Moon Kyu; Jeong, Hoon Eui

    2016-08-31

    We present a simple yet scalable method with detailed process protocols for fabricating dry adhesives with mushroom-shaped micropillars of controlled tip geometries. The method involves using photo-lithography with a bilayer stack combining SU-8 and lift-off resist, and subsequent replica molding process. This approach utilizes widely used and commercially available materials and can thus be used to generate mushroom-shaped micropillars with precisely controlled tip diameters and thicknesses in a simple, reproducible, and cost-effective manner. The fabricated mushroom-shaped micropillar arrays exhibited highly different tendencies in adhesion strength and repeatability depending on tip geometries, such as tip diameter and thickness, thereby demonstrating the importance of precise tunability of tip geometry of micropillars. The fabricated dry adhesives with optimized tip geometries not only exhibited strong pull-off strength of up to ∼34.8 N cm(-2) on the Si surface but also showed high durability. By contrast, dry adhesives with nonoptimized tips displayed low pull-off strength of ∼3.6 N cm(-2) and poor durability. PMID:27548917

  13. Ring beam shaping optics fabricated with ultra-precision cutting for YAG laser processing

    NASA Astrophysics Data System (ADS)

    Kuwano, Ryoichi; Koga, Toshihiko; Tokunaga, Tsuyoshi; Wakayama, Toshitaka; Otani, Yukitoshi; Fujii, Nobuyuki

    2012-03-01

    In this study, a method for generating ring intensity distribution at a refraction-type lens with an aspheric element was proposed, and the beam shaping optical element was finished using only ultra-precision cutting. The shape of the optical element and its irradiance pattern were determined from numerical calculation based on its geometrical and physical optics. An ultra-precision lathe was employed to fabricate beam shaping optical elements, and acrylic resin was used as the material. The transmittance of an optical element (a rotationally symmetrical body) with an aspheric surface fabricated using a single-crystal diamond tool was over 98%, and its surface roughness was 9.6 nm Ra. The method enabled the formation of a circular melting zone on a piece of stainless steel with a thickness of 300 μm through pulse YAG laser ( λ 1:06 μm) processing such that the average radius was 610 μm and the width was 100-200 μm. Circular processing using a ring beam shaping optical element can be realized by single-pulse beam irradiation without beam scanning.

  14. Microfluidic Fabrication of Polymeric and Biohybrid Fibers with Predesigned Size and Shape

    PubMed Central

    Boyd, Darryl A.; Adams, Andre A.; Daniele, Michael A.; Ligler, Frances S.

    2014-01-01

    A “sheath” fluid passing through a microfluidic channel at low Reynolds number can be directed around another “core” stream and used to dictate the shape as well as the diameter of a core stream. Grooves in the top and bottom of a microfluidic channel were designed to direct the sheath fluid and shape the core fluid. By matching the viscosity and hydrophilicity of the sheath and core fluids, the interfacial effects are minimized and complex fluid shapes can be formed. Controlling the relative flow rates of the sheath and core fluids determines the cross-sectional area of the core fluid. Fibers have been produced with sizes ranging from 300 nm to ~1 mm, and fiber cross-sections can be round, flat, square, or complex as in the case with double anchor fibers. Polymerization of the core fluid downstream from the shaping region solidifies the fibers. Photoinitiated click chemistries are well suited for rapid polymerization of the core fluid by irradiation with ultraviolet light. Fibers with a wide variety of shapes have been produced from a list of polymers including liquid crystals, poly(methylmethacrylate), thiol-ene and thiol-yne resins, polyethylene glycol, and hydrogel derivatives. Minimal shear during the shaping process and mild polymerization conditions also makes the fabrication process well suited for encapsulation of cells and other biological components. PMID:24430733

  15. Microfluidic fabrication of polymeric and biohybrid fibers with predesigned size and shape.

    PubMed

    Boyd, Darryl A; Adams, Andre A; Daniele, Michael A; Ligler, Frances S

    2014-01-01

    A "sheath" fluid passing through a microfluidic channel at low Reynolds number can be directed around another "core" stream and used to dictate the shape as well as the diameter of a core stream. Grooves in the top and bottom of a microfluidic channel were designed to direct the sheath fluid and shape the core fluid. By matching the viscosity and hydrophilicity of the sheath and core fluids, the interfacial effects are minimized and complex fluid shapes can be formed. Controlling the relative flow rates of the sheath and core fluids determines the cross-sectional area of the core fluid. Fibers have been produced with sizes ranging from 300 nm to ~1 mm, and fiber cross-sections can be round, flat, square, or complex as in the case with double anchor fibers. Polymerization of the core fluid downstream from the shaping region solidifies the fibers. Photoinitiated click chemistries are well suited for rapid polymerization of the core fluid by irradiation with ultraviolet light. Fibers with a wide variety of shapes have been produced from a list of polymers including liquid crystals, poly(methylmethacrylate), thiol-ene and thiol-yne resins, polyethylene glycol, and hydrogel derivatives. Minimal shear during the shaping process and mild polymerization conditions also makes the fabrication process well suited for encapsulation of cells and other biological components. PMID:24430733

  16. Microstereolithography-Based Fabrication of Anatomically Shaped Beta-Tricalcium Phosphate Scaffolds for Bone Tissue Engineering.

    PubMed

    Du, Dajiang; Asaoka, Teruo; Shinohara, Makoto; Kageyama, Tomonori; Ushida, Takashi; Furukawa, Katsuko Sakai

    2015-01-01

    Porous ceramic scaffolds with shapes matching the bone defects may result in more efficient grafting and healing than the ones with simple geometries. Using computer-assisted microstereolithography (MSTL), we have developed a novel gelcasting indirect MSTL technology and successfully fabricated two scaffolds according to CT images of rabbit femur. Negative resin molds with outer 3D dimensions conforming to the femur and an internal structure consisting of stacked meshes with uniform interconnecting struts, 0.5 mm in diameter, were fabricated by MSTL. The second mold type was designed for cortical bone formation. A ceramic slurry of beta-tricalcium phosphate (β-TCP) with room temperature vulcanization (RTV) silicone as binder was cast into the molds. After the RTV silicone was completely cured, the composite was sintered at 1500°C for 5 h. Both gross anatomical shape and the interpenetrating internal network were preserved after sintering. Even cortical structure could be introduced into the customized scaffolds, which resulted in enhanced strength. Biocompatibility was confirmed by vital staining of rabbit bone marrow mesenchymal stromal cells cultured on the customized scaffolds for 5 days. This fabrication method could be useful for constructing bone substitutes specifically designed according to local anatomical defects. PMID:26504839

  17. Microstereolithography-Based Fabrication of Anatomically Shaped Beta-Tricalcium Phosphate Scaffolds for Bone Tissue Engineering

    PubMed Central

    Du, Dajiang; Asaoka, Teruo; Shinohara, Makoto; Kageyama, Tomonori; Ushida, Takashi; Furukawa, Katsuko Sakai

    2015-01-01

    Porous ceramic scaffolds with shapes matching the bone defects may result in more efficient grafting and healing than the ones with simple geometries. Using computer-assisted microstereolithography (MSTL), we have developed a novel gelcasting indirect MSTL technology and successfully fabricated two scaffolds according to CT images of rabbit femur. Negative resin molds with outer 3D dimensions conforming to the femur and an internal structure consisting of stacked meshes with uniform interconnecting struts, 0.5 mm in diameter, were fabricated by MSTL. The second mold type was designed for cortical bone formation. A ceramic slurry of beta-tricalcium phosphate (β-TCP) with room temperature vulcanization (RTV) silicone as binder was cast into the molds. After the RTV silicone was completely cured, the composite was sintered at 1500°C for 5 h. Both gross anatomical shape and the interpenetrating internal network were preserved after sintering. Even cortical structure could be introduced into the customized scaffolds, which resulted in enhanced strength. Biocompatibility was confirmed by vital staining of rabbit bone marrow mesenchymal stromal cells cultured on the customized scaffolds for 5 days. This fabrication method could be useful for constructing bone substitutes specifically designed according to local anatomical defects. PMID:26504839

  18. Net shape fabrication of calcium phosphate scaffolds with multiple material domains.

    PubMed

    Xie, Yangmin; Rustom, Laurence E; McDermott, Anna M; Boerckel, Joel D; Johnson, Amy J Wagoner; Alleyne, Andrew G; Hoelzle, David J

    2016-01-08

    Calcium phosphate (CaP) materials have been proven to be efficacious as bone scaffold materials, but are difficult to fabricate into complex architectures because of the high processing temperatures required. In contrast, polymeric materials are easily formed into scaffolds with near-net-shape forms of patient-specific defects and with domains of different materials; however, they have reduced load-bearing capacity compared to CaPs. To preserve the merits of CaP scaffolds and enable advanced scaffold manufacturing, this manuscript describes an additive manufacturing process that is coupled with a mold support for overhanging features; we demonstrate that this process enables the fabrication of CaP scaffolds that have both complex, near-net-shape contours and distinct domains with different microstructures. First, we use a set of canonical structures to study the manufacture of complex contours and distinct regions of different material domains within a mold. We then apply these capabilities to the fabrication of a scaffold that is designed for a 5 cm orbital socket defect. This scaffold has complex external contours, interconnected porosity on the order of 300 μm throughout, and two distinct domains of different material microstructures.

  19. Microstereolithography-Based Fabrication of Anatomically Shaped Beta-Tricalcium Phosphate Scaffolds for Bone Tissue Engineering.

    PubMed

    Du, Dajiang; Asaoka, Teruo; Shinohara, Makoto; Kageyama, Tomonori; Ushida, Takashi; Furukawa, Katsuko Sakai

    2015-01-01

    Porous ceramic scaffolds with shapes matching the bone defects may result in more efficient grafting and healing than the ones with simple geometries. Using computer-assisted microstereolithography (MSTL), we have developed a novel gelcasting indirect MSTL technology and successfully fabricated two scaffolds according to CT images of rabbit femur. Negative resin molds with outer 3D dimensions conforming to the femur and an internal structure consisting of stacked meshes with uniform interconnecting struts, 0.5 mm in diameter, were fabricated by MSTL. The second mold type was designed for cortical bone formation. A ceramic slurry of beta-tricalcium phosphate (β-TCP) with room temperature vulcanization (RTV) silicone as binder was cast into the molds. After the RTV silicone was completely cured, the composite was sintered at 1500°C for 5 h. Both gross anatomical shape and the interpenetrating internal network were preserved after sintering. Even cortical structure could be introduced into the customized scaffolds, which resulted in enhanced strength. Biocompatibility was confirmed by vital staining of rabbit bone marrow mesenchymal stromal cells cultured on the customized scaffolds for 5 days. This fabrication method could be useful for constructing bone substitutes specifically designed according to local anatomical defects.

  20. Fabrication

    NASA Astrophysics Data System (ADS)

    Angel, Roger; Helms, Richard; Bilbro, Jim; Brown, Norman; Eng, Sverre; Hinman, Steve; Hull-Allen, Greg; Jacobs, Stephen; Keim, Robert; Ulmer, Melville

    1992-08-01

    What aspects of optical fabrication technology need to be developed so as to facilitate existing planned missions, or enable new ones? Throughout the submillimeter to UV wavelengths, the common goal is to push technology to the limits to make the largest possible apertures that are diffraction limited. At any one wavelength, the accuracy of the surface must be better than lambda/30 (rms error). The wavelength range is huge, covering four orders of magnitude from 1 mm to 100 nm. At the longer wavelengths, diffraction limited surfaces can be shaped with relatively crude techniques. The challenge in their fabrication is to make as large as possible a reflector, given the weight and volume constraints of the launch vehicle. The limited cargo diameter of the shuttle has led in the past to emphasis on deployable or erectable concepts such as the Large Deployable Reflector (LDR), which was studied by NASA for a submillimeter astrophysics mission. Replication techniques that can be used to produce light, low-cost reflecting panels are of great interest for this class of mission. At shorter wavelengths, in the optical and ultraviolet, optical fabrication will tax to the limit the most refined polishing methods. Methods of mechanical and thermal stabilization of the substrate will be severely stressed. In the thermal infrared, the need for large aperture is tempered by the even stronger need to control the telescope's thermal emission by cooled or cryogenic operation. Thus, the SIRTF mirror at 1 meter is not large and does not require unusually high accuracy, but the fabrication process must produce a mirror that is the right shape at a temperature of 4 K. Future large cooled mirrors will present more severe problems, especially if they must also be accurate enough to work at optical wavelengths. At the very shortest wavelengths accessible to reflecting optics, in the x-ray domain, the very low count fluxes of high energy photons place a premium on the collecting area. It is

  1. Fabrication

    NASA Technical Reports Server (NTRS)

    Angel, Roger; Helms, Richard; Bilbro, Jim; Brown, Norman; Eng, Sverre; Hinman, Steve; Hull-Allen, Greg; Jacobs, Stephen; Keim, Robert; Ulmer, Melville

    1992-01-01

    What aspects of optical fabrication technology need to be developed so as to facilitate existing planned missions, or enable new ones? Throughout the submillimeter to UV wavelengths, the common goal is to push technology to the limits to make the largest possible apertures that are diffraction limited. At any one wavelength, the accuracy of the surface must be better than lambda/30 (rms error). The wavelength range is huge, covering four orders of magnitude from 1 mm to 100 nm. At the longer wavelengths, diffraction limited surfaces can be shaped with relatively crude techniques. The challenge in their fabrication is to make as large as possible a reflector, given the weight and volume constraints of the launch vehicle. The limited cargo diameter of the shuttle has led in the past to emphasis on deployable or erectable concepts such as the Large Deployable Reflector (LDR), which was studied by NASA for a submillimeter astrophysics mission. Replication techniques that can be used to produce light, low-cost reflecting panels are of great interest for this class of mission. At shorter wavelengths, in the optical and ultraviolet, optical fabrication will tax to the limit the most refined polishing methods. Methods of mechanical and thermal stabilization of the substrate will be severely stressed. In the thermal infrared, the need for large aperture is tempered by the even stronger need to control the telescope's thermal emission by cooled or cryogenic operation. Thus, the SIRTF mirror at 1 meter is not large and does not require unusually high accuracy, but the fabrication process must produce a mirror that is the right shape at a temperature of 4 K. Future large cooled mirrors will present more severe problems, especially if they must also be accurate enough to work at optical wavelengths. At the very shortest wavelengths accessible to reflecting optics, in the x-ray domain, the very low count fluxes of high energy photons place a premium on the collecting area. It is

  2. Shape, Loading, and Motion in the Bioengineering Design, Fabrication, and Testing of Personalized Synovial Joints

    PubMed Central

    Williams, Gregory M.; Chan, Elaine F.; Temple-Wong, Michele M.; Bae, Won C.; Masuda, Koichi; Bugbee, William D.; Sah, Robert L.

    2009-01-01

    With continued development and improvement of tissue engineering therapies for small articular lesions, increased attention is being focused on the challenge of engineering partial or whole synovial joints. Joint-scale constructs could have applications in the treatment of large areas of articular damage or in biological arthroplasty of severely degenerate joints. This review considers the roles of shape, loading and motion in synovial joint mechanobiology and their incorporation into the design, fabrication, and testing of engineered partial or whole joints. Incidence of degeneration, degree of impairment, and efficacy of current treatments are critical factors in choosing a target for joint bioengineering. The form and function of native joints may guide the design of engineered joint-scale constructs with respect to size, shape, and maturity. Fabrication challenges for joint-scale engineering include controlling chemo-mechano-biological microenvironments to promote the development and growth of multiple tissues with integrated interfaces or lubricated surfaces into anatomical shapes, and joint-scale bioreactors which nurture and stimulate the tissue with loading and motion. Finally, evaluation of load-bearing and tribological properties can range from tissue to joint scale and can focus on biological structure at present or after adaptation. PMID:19815214

  3. A net-shape fabrication process of alumina micro-components using a soft lithography technique

    NASA Astrophysics Data System (ADS)

    Zhu, Zhigang; Wei, Xueyong; Jiang, Kyle

    2007-02-01

    Microceramic components have outstanding properties, such as high temperature resistant, biocompatible, chemically stable and high hardness properties, and could be used in a wide range of applications. However, the fabrication of precision micro-components has long been a barrier and limited their applications. This paper presents a soft lithography technique to fabricate near net-shape alumina micro-components. The process uses elastomer polydimethysiloxane (PDMS) to replace traditional solid moulds and leaves the green patterns intact after demoulding. The whole soft lithography technique involves the following steps: (i) fabricating high aspect ratio SU-8 moulds using UV photolithography, (ii) producing PDMS soft moulds from SU-8 masters, (iii) making aqueous high solids loading alumina suspension, (iv) filling patterned PDMS mould with the aqueous alumina suspension and (v) demoulding and sintering. The rheological properties (zeta potential and viscosity) of aqueous alumina suspensions have been characterized in relation to the varying pH values and concentration of dispersant (D-3005). The optimal parameters of alumina suspension for mould filling have been achieved at a pH value = 11; concentration of dispersant = 0.05 g ml-1; amount of binder = 0.75%; highest solid loading = 70 wt%. After pressurized mould filling, complete, dense and free-standing micro-components have been achieved by using a 70 wt% alumina suspension and an optimum fabrication technique, while the overall linear shrinkage is found to be about 22%.

  4. Understanding deep convolutional networks.

    PubMed

    Mallat, Stéphane

    2016-04-13

    Deep convolutional networks provide state-of-the-art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data with a cascade of linear filter weights and nonlinearities. A mathematical framework is introduced to analyse their properties. Computations of invariants involve multiscale contractions with wavelets, the linearization of hierarchical symmetries and sparse separations. Applications are discussed. PMID:26953183

  5. Effect of various shapes of ZnO nanoparticles on cotton fabric via electrospinning for UV-blocking.

    PubMed

    Neamjan, Natthawut; Sricharussin, Wimonrat; Threepopnatkul, Poonsub

    2012-01-01

    Various shapes of ZnO; multi-petals, rod and spherical were prepared and then applied on cotton fabric for UV-blocking. The ZnO particles were investigated by XRD and SEM. The mixture solution of ZnO with polyvinyl alcohol was applied onto cotton fabrics via electrospinning. The characteristics of the fabric coating were investigated by SEM, XRD, Tensile testing and Atomic absorption spectroscopy (AAS). UV-blocking property was determined by UV-vis spectrophotometer. The results of XRD and SEM on the ZnO powders show that we can produce various shape of ZnO. The investigation by SEM and AAS clearly revealed that ZnO in polyvinyl alcohol nanofibers was effectively deposited on the cotton surface. The sphericals-shaped ZnO coated fabrics show excellent UV-blocking properties. The shape of ZnO shows no considerable effect on the tensile strength of the samples.

  6. Fabrication and perfusion culture of anatomically shaped artificial bone using stereolithography.

    PubMed

    Du, Dajiang; Asaoka, Teruo; Ushida, Takashi; Furukawa, Katsuko S

    2014-09-12

    Because patient bone defects are usually varied and complicated in geometry, it would be preferred to fabricate custom-made artificial bone grafts that are anatomically specific to individual patient defects. Using a rabbit femoral segment as a bone reconstruction model, we successfully produced customized ceramic scaffolds by stereolithography, which not only had an anatomically correct external shape according to computed tomography data but also contained an interconnecting internal network of channels designed for perfusion culture. Rabbit bone marrow stromal cells were isolated and cultured with these scaffolds using a novel oscillatory perfusion system that was stereolithographically fabricated to fit well to the unique scaffold shapes. After five days of three-dimensional culture with oscillatory perfusion, the cells attached and proliferated homogenously in the scaffolds. However, control cells inside the scaffolds cultured under static conditions were dead after prolonged in vitro culture. Cellular DNA content and alkaline phosphatase activities were significantly higher in the perfusion group versus the static group. Therefore, anatomically correct artificial bone can be successfully constructed using stereolithography and oscillatory culture technology, and could be useful for bone engraftment and defect repair.

  7. Free form fabrication using the laser engineered net shaping (LENS{trademark}) process

    SciTech Connect

    Keicher, D.M.; Romero, J.A.; Atwood, C.L.; Griffith, M.L.; Jeantette, F.P.; Harwell, L.D.; Greene, D.L.; Smugeresky, J.E.

    1996-12-31

    Sandia National Laboratories is developing a technology called Laser Engineered Net Shaping{trademark} (LENS{trademark}). This process allows complex 3-dimensional solid metallic objects to be directly fabricated for a CAD solid model. Experiments performed demonstrate that complex alloys such as Inconel{trademark} 625 and ANSI stainless steel alloy 316 can be used in the LENS{trademark} process to produce solid metallic-shapes. In fact, the fabricated structures exhibit grain growth across the deposition layer boundaries. Mechanical testing data of deposited 316 stainless steel material indicates that the deposited material strength and elongation are greater than that reported for annealed 316 stainless steel. Electron microprobe analysis of the deposited Inconel{trademark} 625 material shows no compositional degradation of the 625 alloy and that 100% dense structures can be obtained using this technique. High speed imaging used to acquire process data during experimentation shows that the powder particle size range can significantly affect the stability, and subsequently, the performance of the powder deposition process. Finally, dimensional studies suggest that dimensional accuracy to {+-} 0.002 inches (in the horizontal direction) can be maintained.

  8. Fabrication and characterization of cuprous oxide solar cell with net-shaped counter electrode

    NASA Astrophysics Data System (ADS)

    Basuki, Stefanus; Uranus, Henri P.; Pangaribuan, Julinda

    2015-01-01

    In this work, simple solar cells using cuprous oxide were fabricated and characterized. The solar cells in this experiment used cuprous oxide plate as detecting electrode and copper wires which were woven into a net-shape with a gap size of 2 x 2 cm as a counter electrode. Twenty samples of solar cells were fabricated with oxide layer which were thermally grown in temperature up to 550 oC. Samples with variations in oxidation time (15 minutes, 30 minutes, 40 minutes, and 45 minutes) and distance between electrodes (2 cm, 3 cm, and 4 cm) with an electrolyte solution of NaCl with molarity of 2.188 mol/l were produced. The samples were characterized by measuring their V-I curve. For this purpose, a simple, own-made solar simulator were fabricated and characterized. Using curve fitting technique, parameters such as FF (Fill Factor), efficiency, open circuit voltage, short circuit current, internal resistance, and performance degradation as a function of time of the cells were extracted. The result shows optimum efficiency of 4.573. 10-4%, while optimum oxidation time is 40 minutes and optimum distance between electrodes is 3 cm.

  9. Fabrication and In Vitro Deployment of a Laser-Activated Shape Memory Polymer Vascular Stent

    SciTech Connect

    Baer, G M; Small IV, W; Wilson, T S; Benett, W J; Matthews, D L; Hartman, J; Maitland, D J

    2007-04-25

    Vascular stents are small tubular scaffolds used in the treatment of arterial stenosis (narrowing of the vessel). Most vascular stents are metallic and are deployed either by balloon expansion or by self-expansion. A shape memory polymer (SMP) stent may enhance flexibility, compliance, and drug elution compared to its current metallic counterparts. The purpose of this study was to describe the fabrication of a laser-activated SMP stent and demonstrate photothermal expansion of the stent in an in vitro artery model. A novel SMP stent was fabricated from thermoplastic polyurethane. A solid SMP tube formed by dip coating a stainless steel pin was laser-etched to create the mesh pattern of the finished stent. The stent was crimped over a fiber-optic cylindrical light diffuser coupled to an infrared diode laser. Photothermal actuation of the stent was performed in a water-filled mock artery. At a physiological flow rate, the stent did not fully expand at the maximum laser power (8.6 W) due to convective cooling. However, under zero flow, simulating the technique of endovascular flow occlusion, complete laser actuation was achieved in the mock artery at a laser power of {approx}8 W. We have shown the design and fabrication of an SMP stent and a means of light delivery for photothermal actuation. Though further studies are required to optimize the device and assess thermal tissue damage, photothermal actuation of the SMP stent was demonstrated.

  10. Fabrication and in vitro deployment of a laser-activated shape memory polymer vascular stent

    PubMed Central

    Baer, Géraldine M; Small, Ward; Wilson, Thomas S; Benett, William J; Matthews, Dennis L; Hartman, Jonathan; Maitland, Duncan J

    2007-01-01

    Background Vascular stents are small tubular scaffolds used in the treatment of arterial stenosis (narrowing of the vessel). Most vascular stents are metallic and are deployed either by balloon expansion or by self-expansion. A shape memory polymer (SMP) stent may enhance flexibility, compliance, and drug elution compared to its current metallic counterparts. The purpose of this study was to describe the fabrication of a laser-activated SMP stent and demonstrate photothermal expansion of the stent in an in vitro artery model. Methods A novel SMP stent was fabricated from thermoplastic polyurethane. A solid SMP tube formed by dip coating a stainless steel pin was laser-etched to create the mesh pattern of the finished stent. The stent was crimped over a fiber-optic cylindrical light diffuser coupled to an infrared diode laser. Photothermal actuation of the stent was performed in a water-filled mock artery. Results At a physiological flow rate, the stent did not fully expand at the maximum laser power (8.6 W) due to convective cooling. However, under zero flow, simulating the technique of endovascular flow occlusion, complete laser actuation was achieved in the mock artery at a laser power of ~8 W. Conclusion We have shown the design and fabrication of an SMP stent and a means of light delivery for photothermal actuation. Though further studies are required to optimize the device and assess thermal tissue damage, photothermal actuation of the SMP stent was demonstrated. PMID:18042294

  11. A fabrication method of unique Nafion® shapes by painting for ionic polymer–metal composites

    NASA Astrophysics Data System (ADS)

    Trabia, Sarah; Hwang, Taeseon; Kim, Kwang J.

    2016-08-01

    Ionic polymer–metal composites (IPMC) are useful actuators because of their ability to be fabricated in different shapes and move in various ways. However, producing unique or intricate shapes can be difficult based upon the current fabrication techniques. Presented here is a fabrication method of producing the Nafion® membrane or thin film through a painting method. Using an airbrush, a Nafion water dispersion is sprayed onto an acrylonitrile butadiene styrene surface with a stencil of the desired shape. To verify that this method of fabrication produces a Nafion membrane similar to that which is commercially available, a sample that was made using the painting method and Nafion 117 purchased from DuPont™ were tested for various characteristics and compared. The results show promising similarities. The painted Nafion sample was chemically plated with platinum and compared with a traditional IPMC for its displacement and blocking force capabilities. The painted IPMC sample showed comparable results.

  12. Facile moldless fabrication of disk-shaped and reed blood cell-like microparticles using photopolymerization of tripropylene glycol diacrylate

    NASA Astrophysics Data System (ADS)

    Choi, Jongchul; Won, June; Song, Simon

    2014-12-01

    A facile method for the moldless fabrication of 2- or 3-dimensional microparticles is proposed by using a photopolymerization technique. Using only a monomer solution of tripropylene glycol diacrylate, a film mask and standard UV lithography equipment, we were able to fabricate microparticles of various shapes, such as disks, dimpled disks similar in shape to red blood cells, and slender gourd shapes, unlike previous moldless fabrication techniques requiring expensive and/or sophisticated equipment. The simple method could produce more than one million particles in a single batch, indicating that it can be applied to the mass production of polymer microparticles. Analyses of scanning electron micrographs and optical micrographs of the microparticles indicated that their size distribution was highly monodisperse. Detailed fabrication processes and statistics on the microparticle sizes are given in this paper.

  13. A fabrication method of unique Nafion® shapes by painting for ionic polymer-metal composites

    NASA Astrophysics Data System (ADS)

    Trabia, Sarah; Hwang, Taeseon; Kim, Kwang J.

    2016-08-01

    Ionic polymer-metal composites (IPMC) are useful actuators because of their ability to be fabricated in different shapes and move in various ways. However, producing unique or intricate shapes can be difficult based upon the current fabrication techniques. Presented here is a fabrication method of producing the Nafion® membrane or thin film through a painting method. Using an airbrush, a Nafion water dispersion is sprayed onto an acrylonitrile butadiene styrene surface with a stencil of the desired shape. To verify that this method of fabrication produces a Nafion membrane similar to that which is commercially available, a sample that was made using the painting method and Nafion 117 purchased from DuPont™ were tested for various characteristics and compared. The results show promising similarities. The painted Nafion sample was chemically plated with platinum and compared with a traditional IPMC for its displacement and blocking force capabilities. The painted IPMC sample showed comparable results.

  14. Properties of near-net shape metallic components made by the directed light fabrication process

    SciTech Connect

    Lewis, G.K.; Milewski, J.O.; Thoma, D.B.; Nemec, R.B.

    1997-10-01

    Directed Light Fabrication (DLF) is a process invented at Los Alamos National Laboratory that can be used to fuse any metal powder directly to a fully dense, near-net shape component with full structural integrity. A solid model design of a desired component is first developed on a computer work station. A motion path, produced from the solid model definition, is translated to actual machine commands through a post-processor, specific to the deposition equipment. The DLF process uses a multi-axis positioning system to move the laser focal zone over the part cross section defined by the part boundaries and desired layer thickness. Metal powders, delivered in an argon stream, enter the focal zone where they melt and continuously form a molten pool of material that moves with the laser focal spot. Position and movement of the spot is controlled through the post-processor. Successive cross-sectional layers are added by advancing the spot one layer thickness beyond the previous layer until the entire part is deposited. The system has 4 powder feeders attached for co-deposition of multiple materials to create alloys at the focal zone or form dissimilar metal joint combinations by changing powder composition from one material to another. Parts produced by the DLF process vary in complexity from simple bulk solid forms to detailed components fabricated from difficult to process metals and alloys. Parts have been deposited at rates up to 33 cm{sup 3}/hr with 12 cm{sup 3}/hr more typical. Feasibility of processing any metal ranging in melting point from aluminium to tungsten has been demonstrated. Mechanical properties for bulk DLF deposits of three alloy powders were measured for this study. Ti-6Al-4V and 316 stainless steel powders were fabricated into rectangular bar, and Inconel 690 powder was fabricated into a solid cylinder.

  15. Fabrication of shape controlled Fe{sub 3}O{sub 4} nanostructure

    SciTech Connect

    Zheng, Y.Y.; Wang, X.B.; Shang, L.; Li, C.R.; Cui, C.; Dong, W.J.; Tang, W.H.; Chen, B.Y.

    2010-04-15

    Shape-controlled Fe{sub 3}O{sub 4} nanostructure has been successfully prepared using polyethylene glycol as template in a water system at room temperature. Different morphologies of Fe{sub 3}O{sub 4} nanostructures, including spherical, cubic, rod-like, and dendritic nanostructure, were obtained by carefully controlling the concentration of the Fe{sup 3+}, Fe{sup 2+}, and the molecular weight of the polyethylene glycol. Transmission Electron Microscope images, X-ray powder diffraction patterns and magnetic properties were used to characterize the final product. This easy procedure for Fe{sub 3}O{sub 4} nanostructure fabrication offers the possibility of a generalized approach to the production of single and complex nanocrystalline oxide with tunable morphology.

  16. Fabrication and Characterization of Cylindrical Light Diffusers Comprised of Shape Memory Polymer

    SciTech Connect

    Small IV, W; Buckley, P R; Wilson, T S; Loge, J M; Maitland, K D; Maitland, D J

    2007-01-29

    We have developed a technique for constructing light diffusing devices comprised of a flexible shape memory polymer (SMP) cylindrical diffuser attached to the tip of an optical fiber. Devices were fabricated by casting an SMP rod over the cleaved tip of an optical fiber and media blasting the SMP rod to create a light diffusing surface. The axial and polar emission profiles and circumferential (azimuthal) uniformity were characterized for various blasting pressures, nozzle-to-sample distances, and nozzle translation speeds. The diffusers were generally strongly forward-directed and consistently withstood over 8 W of incident infrared laser light without suffering damage when immersed in water. These devices are suitable for various endoluminal and interstitial biomedical applications.

  17. Lipid Nanotube Tailored Fabrication of Uniquely Shaped Polydopamine Nanofibers as Photothermal Converters.

    PubMed

    Ding, Wuxiao; Chechetka, Svetlana A; Masuda, Mitsutoshi; Shimizu, Toshimi; Aoyagi, Masaru; Minamikawa, Hiroyuki; Miyako, Eijiro

    2016-03-18

    Helically coiled and linear polydopamine (PDA) nanofibers were selectively fabricated with two different types of lipid nanotubes (LNTs) that acted as templates. The obtained coiled PDA-LNT hybrid showed morphological advantages such as higher light absorbance and photothermal conversion effect compared to a linear counterpart. Laser irradiation of the coiled PDA-LNT hybrid induced a morphological change and subsequent release of the encapsulated guest molecule. In cellular experiments, the coiled PDA-LNT efficiently eliminated HeLa cells because of its strong affinity with the tumor cells. This work illustrates the first approach to construct characteristic morphologies of PDA nanofibers using LNTs as simple templates, and the coiled PDA-LNT hybrid exhibits attractive photothermal features derived from its unique coiled shape.

  18. Facile 3D Metal Electrode Fabrication for Energy Applications via Inkjet Printing and Shape Memory Polymer

    NASA Astrophysics Data System (ADS)

    Roberts, R. C.; Wu, J.; Hau, N. Y.; Chang, Y. H.; Feng, S. P.; Li, D. C.

    2014-11-01

    This paper reports on a simple 3D metal electrode fabrication technique via inkjet printing onto a thermally contracting shape memory polymer (SMP) substrate. Inkjet printing allows for the direct patterning of structures from metal nanoparticle bearing liquid inks. After deposition, these inks require thermal curing steps to render a stable conductive film. By printing onto a SMP substrate, the metal nanoparticle ink can be cured and substrate shrunk simultaneously to create 3D metal microstructures, forming a large surface area topology well suited for energy applications. Polystyrene SMP shrinkage was characterized in a laboratory oven from 150-240°C, resulting in a size reduction of 1.97-2.58. Silver nanoparticle ink was patterned into electrodes, shrunk, and the topology characterized using scanning electron microscopy. Zinc-Silver Oxide microbatteries were fabricated to demonstrate the 3D electrodes compared to planar references. Characterization was performed using 10M potassium hydroxide electrolyte solution doped with zinc oxide (57g/L). After a 300s oxidation at 3Vdc, the 3D electrode battery demonstrated a 125% increased capacity over the reference cell. Reference cells degraded with longer oxidations, but the 3D electrodes were fully oxidized for 4 hours, and exhibited a capacity of 5.5mA-hr/cm2 with stable metal performance.

  19. Near-Net Shape Fabrication Using Low-Cost Titanium Alloy Powders

    SciTech Connect

    Dr. David M. Bowden; Dr. William H. Peter

    2012-03-31

    The use of titanium in commercial aircraft production has risen steadily over the last half century. The aerospace industry currently accounts for 58% of the domestic titanium market. The Kroll process, which has been used for over 50 years to produce titanium metal from its mineral form, consumes large quantities of energy. And, methods used to convert the titanium sponge output of the Kroll process into useful mill products also require significant energy resources. These traditional approaches result in product forms that are very expensive, have long lead times of up to a year or more, and require costly operations to fabricate finished parts. Given the increasing role of titanium in commercial aircraft, new titanium technologies are needed to create a more sustainable manufacturing strategy that consumes less energy, requires less material, and significantly reduces material and fabrication costs. A number of emerging processes are under development which could lead to a breakthrough in extraction technology. Several of these processes produce titanium alloy powder as a product. The availability of low-cost titanium powders may in turn enable a more efficient approach to the manufacture of titanium components using powder metallurgical processing. The objective of this project was to define energy-efficient strategies for manufacturing large-scale titanium structures using these low-cost powders as the starting material. Strategies include approaches to powder consolidation to achieve fully dense mill products, and joining technologies such as friction and laser welding to combine those mill products into near net shape (NNS) preforms for machining. The near net shape approach reduces material and machining requirements providing for improved affordability of titanium structures. Energy and cost modeling was used to define those approaches that offer the largest energy savings together with the economic benefits needed to drive implementation. Technical

  20. Glass-on-Glass Fabrication of Bottle-Shaped Tunable Microlasers and their Applications.

    PubMed

    Ward, Jonathan M; Yang, Yong; Nic Chormaic, Síle

    2016-01-01

    We describe a novel method for making microbottle-shaped lasers by using a CO2 laser to melt Er:Yb glass onto silica microcapillaries or fibres. This is realised by the fact that the two glasses have different melting points. The CO2 laser power is controlled to flow the doped glass around the silica cylinder. In the case of a capillary, the resulting geometry is a hollow, microbottle-shaped resonator. This is a simple method for fabricating a number of glass whispering gallery mode (WGM) lasers with a wide range of sizes on a single, micron-scale structure. The Er:Yb doped glass outer layer is pumped at 980 nm via a tapered optical fibre and WGM lasing is recorded around 1535 nm. This structure facilitates a new way to thermo-optically tune the microlaser modes by passing gas through the capillary. The cooling effect of the gas flow shifts the WGMs towards shorter wavelengths and thermal tuning of the lasing modes over 70 GHz is achieved. Results are fitted using the theory of hot wire anemometry, allowing the flow rate to be calibrated with a flow sensitivity as high as 72 GHz/sccm. Strain tuning of the microlaser modes by up to 60 GHz is also demonstrated. PMID:27121151

  1. Glass-on-Glass Fabrication of Bottle-Shaped Tunable Microlasers and their Applications

    NASA Astrophysics Data System (ADS)

    Ward, Jonathan M.; Yang, Yong; Nic Chormaic, Síle

    2016-04-01

    We describe a novel method for making microbottle-shaped lasers by using a CO2 laser to melt Er:Yb glass onto silica microcapillaries or fibres. This is realised by the fact that the two glasses have different melting points. The CO2 laser power is controlled to flow the doped glass around the silica cylinder. In the case of a capillary, the resulting geometry is a hollow, microbottle-shaped resonator. This is a simple method for fabricating a number of glass whispering gallery mode (WGM) lasers with a wide range of sizes on a single, micron-scale structure. The Er:Yb doped glass outer layer is pumped at 980 nm via a tapered optical fibre and WGM lasing is recorded around 1535 nm. This structure facilitates a new way to thermo-optically tune the microlaser modes by passing gas through the capillary. The cooling effect of the gas flow shifts the WGMs towards shorter wavelengths and thermal tuning of the lasing modes over 70 GHz is achieved. Results are fitted using the theory of hot wire anemometry, allowing the flow rate to be calibrated with a flow sensitivity as high as 72 GHz/sccm. Strain tuning of the microlaser modes by up to 60 GHz is also demonstrated.

  2. An innovative method and experiment for fabricating bulgy shape nanochannel using AFM

    NASA Astrophysics Data System (ADS)

    Lin, Zone-Ching; Jheng, Hao-Yuan; Ding, Hao-Yang

    2015-08-01

    The paper proposes using atomic force microscopy (AFM) and the concept of specific down force energy (SDFE) to establish an innovative offset cycle cutting method for fabricating a bulgy shape nanochannel on a single-crystal silicon substrate. In the offset cycle cutting method, cutting is performed at a constant down force in all cutting passes. After the first cutting pass, the AFM probe is offset rightward for the second pass and subsequently offset leftward to the middle (i.e., between the positions of the first two cutting passes) for the third cutting pass. Applying a step-by-step method to modify the offset distance and approach the defined SDFE value, this study determined the depth of the middle cutting pass and smaller values of upward bulginess and downward indentation at the bottom of the nanochannel. The nanochannel width can be increased by increasing the number of offset cycle cutting passes. In addition, by applying the proposed method, this study involved a simulation and experiment concerning the cutting path plan of bulgy shape nanochannels. Furthermore, using a small down force along the burr path is proposed for reducing burr height. The results of the simulation and experiment were compared to verify the feasibility of the method.

  3. Fabrication and lithium storage performance of sugar apple-shaped SiOx@C nanocomposite spheres

    NASA Astrophysics Data System (ADS)

    Li, Mingqi; Zeng, Ying; Ren, Yurong; Zeng, Chunmei; Gu, Jingwei; Feng, Xiaofang; He, Hongyan

    2015-08-01

    Nonstoichiometric SiOx is a kind of very attractive anode material for high-energy lithium-ion batteries because of a high specific capacity and facile synthesis. However, the poor electrical conductivity and unstable electrode structure of SiOx severely limit its electrochemical performance as anode in lithium-ion batteries. In this work, highly durable sugar apple-shaped SiOx@C nanocomposite spheres are fabricated to achieve significantly improved electrochemical performance. The composite is synthesized by homogenous one-pot synthesis, using ethyltriethoxysilanes (EtSi(OEt)3) and resorcinol/formaldehyde (RF) as starting materials. The morphology, composition and structure of the composite are investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental analysis (EA) and X-ray photoelectron spectroscopy (XPS). At a current density of 50 mA g-1, the sugar apple-shaped SiOx@C spheres exhibit a stable discharge capacity of about 630 mAh g-1 calculated on the total mass of both SiOx and C. At a current density of 100 mA g-1, a stable discharge capacity of about 550 mAh g-1 is obtained and the capacity has been kept up to 400 cycles. The excellent cycling performance is attributed to the homogeneous dispersion of SiOx in disordered carbon at the nanometer scale and the unique structure of the composite.

  4. Glass-on-Glass Fabrication of Bottle-Shaped Tunable Microlasers and their Applications

    PubMed Central

    Ward, Jonathan M.; Yang, Yong; Nic Chormaic, Síle

    2016-01-01

    We describe a novel method for making microbottle-shaped lasers by using a CO2 laser to melt Er:Yb glass onto silica microcapillaries or fibres. This is realised by the fact that the two glasses have different melting points. The CO2 laser power is controlled to flow the doped glass around the silica cylinder. In the case of a capillary, the resulting geometry is a hollow, microbottle-shaped resonator. This is a simple method for fabricating a number of glass whispering gallery mode (WGM) lasers with a wide range of sizes on a single, micron-scale structure. The Er:Yb doped glass outer layer is pumped at 980 nm via a tapered optical fibre and WGM lasing is recorded around 1535 nm. This structure facilitates a new way to thermo-optically tune the microlaser modes by passing gas through the capillary. The cooling effect of the gas flow shifts the WGMs towards shorter wavelengths and thermal tuning of the lasing modes over 70 GHz is achieved. Results are fitted using the theory of hot wire anemometry, allowing the flow rate to be calibrated with a flow sensitivity as high as 72 GHz/sccm. Strain tuning of the microlaser modes by up to 60 GHz is also demonstrated. PMID:27121151

  5. Fabrication of well-defined mushroom-shaped structures for biomimetic dry adhesive by conventional photolithography and molding.

    PubMed

    Wang, Yue; Hu, Hong; Shao, Jinyou; Ding, Yucheng

    2014-02-26

    Biomimetic dry adhesives have many attractive features, such as reversible and repeatable adhesion against various surfaces. This paper presents a method for the simple fabrication of biomimetic dry adhesives composed of a mushroom-shaped structure, which is based on conventional photolithography and molding. Firstly a masked and a maskless exposure are performed on the top and bottom of a photoresist, respectively, that generates microholes with an undercut after development. This structured photoresist is then used for molding, leading to mushroom-shaped structural features after sacrificing the photoresist. Because of the convenience of photolithography, the proposed method has the potential to fabricate various dry adhesives cost-efficiently.

  6. Dealiased convolutions for pseudospectral simulations

    NASA Astrophysics Data System (ADS)

    Roberts, Malcolm; Bowman, John C.

    2011-12-01

    Efficient algorithms have recently been developed for calculating dealiased linear convolution sums without the expense of conventional zero-padding or phase-shift techniques. For one-dimensional in-place convolutions, the memory requirements are identical with the zero-padding technique, with the important distinction that the additional work memory need not be contiguous with the input data. This decoupling of data and work arrays dramatically reduces the memory and computation time required to evaluate higher-dimensional in-place convolutions. The memory savings is achieved by computing the in-place Fourier transform of the data in blocks, rather than all at once. The technique also allows one to dealias the n-ary convolutions that arise on Fourier transforming cubic and higher powers. Implicitly dealiased convolutions can be built on top of state-of-the-art adaptive fast Fourier transform libraries like FFTW. Vectorized multidimensional implementations for the complex and centered Hermitian (pseudospectral) cases have already been implemented in the open-source software FFTW++. With the advent of this library, writing a high-performance dealiased pseudospectral code for solving nonlinear partial differential equations has now become a relatively straightforward exercise. New theoretical estimates of computational complexity and memory use are provided, including corrected timing results for 3D pruned convolutions and further consideration of higher-order convolutions.

  7. New optimal quantum convolutional codes

    NASA Astrophysics Data System (ADS)

    Zhu, Shixin; Wang, Liqi; Kai, Xiaoshan

    2015-04-01

    One of the most challenges to prove the feasibility of quantum computers is to protect the quantum nature of information. Quantum convolutional codes are aimed at protecting a stream of quantum information in a long distance communication, which are the correct generalization to the quantum domain of their classical analogs. In this paper, we construct some classes of quantum convolutional codes by employing classical constacyclic codes. These codes are optimal in the sense that they attain the Singleton bound for pure convolutional stabilizer codes.

  8. Entanglement-assisted quantum convolutional coding

    SciTech Connect

    Wilde, Mark M.; Brun, Todd A.

    2010-04-15

    We show how to protect a stream of quantum information from decoherence induced by a noisy quantum communication channel. We exploit preshared entanglement and a convolutional coding structure to develop a theory of entanglement-assisted quantum convolutional coding. Our construction produces a Calderbank-Shor-Steane (CSS) entanglement-assisted quantum convolutional code from two arbitrary classical binary convolutional codes. The rate and error-correcting properties of the classical convolutional codes directly determine the corresponding properties of the resulting entanglement-assisted quantum convolutional code. We explain how to encode our CSS entanglement-assisted quantum convolutional codes starting from a stream of information qubits, ancilla qubits, and shared entangled bits.

  9. A fabrication method of unique Nafion shapes by painting for ionic polymer-metal composites (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Trabia, Sarah; Hwang, Taeseon; Kim, Kwang Jin

    2016-04-01

    Ionic Polymer-Metal Composites (IPMC) are useful actuators because of their ability to be fabricated in different shapes and move in various ways. However, the process to produce an IPMC is complicated and takes a few days. To make it possible to mass produce in any desired shape, the fabrication process must be updated. Presented here is a new way of producing the Nafion® base through a spraying method, then the electrode will be plated with spraying method as well. To verify that this method of fabrication produces a Nafion® sample similar to that which is commercially available, a sample that was made using spraying method and N117 purchased from DuPont™ were tested for various characteristics and compared.

  10. Fabrication of a bowl-shaped silver cavity substrate for SERS-based immunoassay.

    PubMed

    Tian, Shu; Zhou, Qun; Gu, Zhuomin; Gu, Xuefang; Zheng, Junwei

    2013-05-01

    In this study, a metal sandwich substrate bridged by an immunocomplex has been created for a surface enhanced Raman scattering (SERS)-based immunoassay. The bottom bowl-shaped silver cavity thin film layer was prepared by electrodeposition using a closely packed monolayer of 700 nm diameter polystyrene spheres as a template. The reflection spectra of the films were recorded as a function of film thickness, and then correlated with SERS enhancement using p-aminothiophenol as the probe molecule. The results demonstrate that SERS enhancement can be maximized when both the frequency of the incident laser and Raman scattering approach the resonance frequency of the localized surface plasmon resonance, providing a guideline for the fabrication and further application of these nanocavity arrays. The second layer of silver was introduced by the interactions between the immunocomplexes in the middle layer of the sandwich architecture and the silver nanoparticles. The proposed structure was used to perform the SERS-based immunoassay. The labeled protein can be detected over a wide concentration range and the detection limit of TRITC and Atto610 labeled proteins were 50 and 5 pg mL(-1), respectively. The results demonstrate that the new SERS substrate is suitable for the quantitative identification of biomolecules. PMID:23476921

  11. Fabrication and surface photovoltage study of hematite microparticles with hollow spindle-shaped structure

    NASA Astrophysics Data System (ADS)

    Li, Hong; Zhao, Qidong; Li, Xinyong; Shi, Yong; Chen, Guohua

    2012-07-01

    Hematite (α-Fe2O3) particles with hollow spindle-shaped microstructure were successfully synthesized by a one-pot hydrothermal approach in large scale. The structural properties of the sample were systematically investigated by X-ray powder diffraction, scanning electron microscopy, energy-dispersive X-ray spectrum, high resolution transmission electron microscopy, selected-area electron diffraction techniques, UV-vis diffuse reflectance spectroscopy and infrared spectroscopy techniques. The characterization results revealed that the α-Fe2O3 microparticles with a single-domain crystalline structure was mainly grown along the (1 0 4) crystal plane. The valence states and the surface chemical compositions of α-Fe2O3 were further identified by X-ray photoelectron spectroscopy. The feature of photo-induced charge separation on spectrum was demonstrated by the surface photovoltage measurement under different external biases. The observed photoelectric characteristics of the as-fabricated material are beneficial for various optical and electronic applications.

  12. A Novel Shape Memory Alloy Annuloplasty Ring for Minimally Invasive Surgery: Design, Fabrication, and Evaluation

    PubMed Central

    Purser, Molly F.; Richards, Andrew L.; Cook, Richard C.; Osborne, Jason A.; Cormier, Denis R.; Buckner, Gregory D.

    2013-01-01

    A novel annuloplasty ring with a shape memory alloy core has been developed to facilitate minimally invasive mitral valve repair. In its activated (austenitic) phase, this prototype ring has comparable mechanical properties to commercial semi-rigid rings. In its pre-activated (martensitic) phase, this ring is flexible enough to be introduced through an 8-mm trocar and easily manipulated with robotic instruments within the confines of a left atrial model. The core is constructed of 0.50 mm diameter NiTi, which is maintained below its martensitic transition temperature (24 °C) during deployment and suturing. After suturing, the ring is heated above its austenitic transition temperature (37 °C, normal human body temperature) enabling the NiTi core to attain its optimal geometry and stiffness characteristics indefinitely. This article summarizes the design, fabrication, and evaluation of this prototype ring. Experimental results suggest that the NiTi core ring could be a viable alternative to flexible bands in robot-assisted minimally invasive mitral valve repair. PMID:20652747

  13. Fabrication of 10 nm-scale complex 3D nanopatterns with multiple shapes and components by secondary sputtering phenomenon.

    PubMed

    Jeon, Hwan-Jin; Jeong, Hyeon Su; Kim, Yun Ho; Jung, Woo-Bin; Kim, Jeong Yeon; Jung, Hee-Tae

    2014-02-25

    We introduce an advanced ultrahigh-resolution (∼ 15 nm) patterning technique that enables the fabrication of various 3D high aspect ratio multicomponents/shaped nanostructures. This methodology utilizes the repetitive secondary sputtering phenomenon under etching plasma conditions and prepatterned fabrication control. The secondary sputtering phenomenon repetitively generates an angular distribution of target particles during ion-bombardment. This method, advanced repetitive secondary sputtering lithography, provides many strategies to fabricate complex continuous patterns and multilayer/material patterns with 10 nm-scale resolution. To demonstrate the versatility of this method, we show induced vertical alignment of liquid crystals (LCs) on indium-tin-oxide (ITO) grid patterns without any alignment layers. The ITO grid pattern fabricated in this method is found to have not only an alignment capability but also electrode properties without electrical or optical damage.

  14. Fabrication and evaluation results of a micro elliptical collimator lens for a beam shape form of laser diode

    NASA Astrophysics Data System (ADS)

    Okada, K.; Oohira, F.; Hosogi, M.; Hashiguchi, G.; Mihara, Y.; Ogawa, K.

    2005-12-01

    This paper describes a new fabrication process of a micro elliptical collimator lens to form a beam shape for LD(Laser Diode), and the evaluation results of the optical characteristic for this lens. Beam shape of LD is an ellipse because divergent light angle is different between horizontal and vertical direction, which increases a coupling loss with an optical fiber. In this presentation, we propose the lens to form the divergent light of an elliptical beam shape to the collimated light of a circular beam shape. This lens makes it possible to reduce the coupling loss with the optical fiber. For this purpose, we designed one lens, which has different curvature radiuses between incident and output surfaces. In the incident surface, the divergent light is formed to the convergent light, and in the output surface, the convergent light is formed to the collimated light. We simulated the optical characteristic of this lens, and designed for various parameters. In order to fabricate this lens, we propose a new process using a chemically absorbed monomolecular layer, which has an excellent hydrophobic property. This layer is patterned and deposited by a photolithographic technique. Next, we drop a UV(Ultra Violet) cure material on the hydrophilic area, as the result, we can fabricate a micro elliptical lens shape. The curvature radius of this lens can be controlled by the amount of a dropped UV cure material and an elliptical pattern size in horizontal and vertical direction. The formed lens shapes are transferred by the electro-plating and then the micro dies are fabricated. And they are used for molding the plastic lens.

  15. Die and telescoping punch form convolutions in thin diaphragm

    NASA Technical Reports Server (NTRS)

    1965-01-01

    Die and punch set forms convolutions in thin dished metal diaphragm without stretching the metal too thin at sharp curvatures. The die corresponds to the metal shape to be formed, and the punch consists of elements that progressively slide against one another under the restraint of a compressed-air cushion to mate with the die.

  16. Distal convoluted tubule.

    PubMed

    McCormick, James A; Ellison, David H

    2015-01-01

    The distal convoluted tubule (DCT) is a short nephron segment, interposed between the macula densa and collecting duct. Even though it is short, it plays a key role in regulating extracellular fluid volume and electrolyte homeostasis. DCT cells are rich in mitochondria, and possess the highest density of Na+/K+-ATPase along the nephron, where it is expressed on the highly amplified basolateral membranes. DCT cells are largely water impermeable, and reabsorb sodium and chloride across the apical membrane via electroneurtral pathways. Prominent among this is the thiazide-sensitive sodium chloride cotransporter, target of widely used diuretic drugs. These cells also play a key role in magnesium reabsorption, which occurs predominantly, via a transient receptor potential channel (TRPM6). Human genetic diseases in which DCT function is perturbed have provided critical insights into the physiological role of the DCT, and how transport is regulated. These include Familial Hyperkalemic Hypertension, the salt-wasting diseases Gitelman syndrome and EAST syndrome, and hereditary hypomagnesemias. The DCT is also established as an important target for the hormones angiotensin II and aldosterone; it also appears to respond to sympathetic-nerve stimulation and changes in plasma potassium. Here, we discuss what is currently known about DCT physiology. Early studies that determined transport rates of ions by the DCT are described, as are the channels and transporters expressed along the DCT with the advent of molecular cloning. Regulation of expression and activity of these channels and transporters is also described; particular emphasis is placed on the contribution of genetic forms of DCT dysregulation to our understanding.

  17. Fabrication of volcano-shaped nano-patterned sapphire substrates using colloidal self-assembly and wet chemical etching.

    PubMed

    Geng, Chong; Zheng, Lu; Fang, Huajing; Yan, Qingfeng; Wei, Tongbo; Hao, Zhibiao; Wang, Xiaoqing; Shen, Dezhong

    2013-08-23

    Patterned sapphire substrates (PSS) have been widely used to enhance the light output power in GaN-based light emitting diodes. The shape and feature size of the pattern in a PSS affect its enhancement efficiency to a great degree. In this work we demonstrate the nanoscale fabrication of volcano-shaped PSS using a wet chemical etching approach in combination with a colloidal monolayer templating strategy. Detailed analysis by scanning electron microscopy reveals that the unique pattern shape is a result of the different corrosion-resistant abilities of silica masks of different effective heights during wet chemical etching. The formation of silica etching masks of different effective heights has been ascribed to the silica precursor solution in the interstice of the colloidal monolayer template being distributed unevenly after infiltration. In the subsequent wet chemical etching process, the active reaction sites altered as etching duration was prolonged, resulting in the formation of volcano-shaped nano-patterned sapphire substrates.

  18. Fabrication of petal-shaped masks for suppression of the on-axis Poisson spot in telescope systems

    NASA Astrophysics Data System (ADS)

    Shiri, Ron; Stein, Ryan; Murphy, Kaitlin; Hagopian, Kimberly; Salari, Shirin; Sankar, Shannon; Hagopian, John; Showalter, Matthew; Stevenson, Thomas; Quijada, Manuel; Threat, Felix; Friedlander, Jay; Dillon, Thomas; Livas, Jeffrey

    2016-04-01

    The presence of a bright (Poisson) spot in the geometrical shadow of circular/spherical shapes has been known for the past two centuries. A broad class of telescopes that involve simultaneous transmit and receive require suppression of the reflected light from the secondary mirror on the detector. For instance, the on-axis design of optical telescope for the evolved Laser Interferometric Space Antenna (eLISA), a re-scoped version of the baseline LISA mission concept, requires suppression of reflected laser light from the secondary mirror on the detector. In the past few years, the hypergaussian functions with petal-shaped realization have been shown to significantly suppress intensity along the optical axis. This work reports on fabrication of a series of petal-shaped masks using a variety of techniques such as 3D printing, photolithography, and wire Electro Discharge Machining. These masks are designed and fabricated to operate in the range of Fresnel numbers between 4 and 120. This paper discusses the challenges, successes, and failures of each fabrication technique and the optical performance of typical masks with suggestions for potential follow up work.

  19. Fabrication of petal-shaped masks for suppression of the on-axis Poisson spot in telescope systems.

    PubMed

    Shiri, Ron; Stein, Ryan; Murphy, Kaitlin; Hagopian, Kimberly; Salari, Shirin; Sankar, Shannon; Hagopian, John; Showalter, Matthew; Stevenson, Thomas; Quijada, Manuel; Threat, Felix; Friedlander, Jay; Dillon, Thomas; Livas, Jeffrey

    2016-04-01

    The presence of a bright (Poisson) spot in the geometrical shadow of circular/spherical shapes has been known for the past two centuries. A broad class of telescopes that involve simultaneous transmit and receive require suppression of the reflected light from the secondary mirror on the detector. For instance, the on-axis design of optical telescope for the evolved Laser Interferometric Space Antenna (eLISA), a re-scoped version of the baseline LISA mission concept, requires suppression of reflected laser light from the secondary mirror on the detector. In the past few years, the hypergaussian functions with petal-shaped realization have been shown to significantly suppress intensity along the optical axis. This work reports on fabrication of a series of petal-shaped masks using a variety of techniques such as 3D printing, photolithography, and wire Electro Discharge Machining. These masks are designed and fabricated to operate in the range of Fresnel numbers between 4 and 120. This paper discusses the challenges, successes, and failures of each fabrication technique and the optical performance of typical masks with suggestions for potential follow up work. PMID:27131659

  20. Fabrication of petal-shaped masks for suppression of the on-axis Poisson spot in telescope systems.

    PubMed

    Shiri, Ron; Stein, Ryan; Murphy, Kaitlin; Hagopian, Kimberly; Salari, Shirin; Sankar, Shannon; Hagopian, John; Showalter, Matthew; Stevenson, Thomas; Quijada, Manuel; Threat, Felix; Friedlander, Jay; Dillon, Thomas; Livas, Jeffrey

    2016-04-01

    The presence of a bright (Poisson) spot in the geometrical shadow of circular/spherical shapes has been known for the past two centuries. A broad class of telescopes that involve simultaneous transmit and receive require suppression of the reflected light from the secondary mirror on the detector. For instance, the on-axis design of optical telescope for the evolved Laser Interferometric Space Antenna (eLISA), a re-scoped version of the baseline LISA mission concept, requires suppression of reflected laser light from the secondary mirror on the detector. In the past few years, the hypergaussian functions with petal-shaped realization have been shown to significantly suppress intensity along the optical axis. This work reports on fabrication of a series of petal-shaped masks using a variety of techniques such as 3D printing, photolithography, and wire Electro Discharge Machining. These masks are designed and fabricated to operate in the range of Fresnel numbers between 4 and 120. This paper discusses the challenges, successes, and failures of each fabrication technique and the optical performance of typical masks with suggestions for potential follow up work.

  1. Nonbinary Quantum Convolutional Codes Derived from Negacyclic Codes

    NASA Astrophysics Data System (ADS)

    Chen, Jianzhang; Li, Jianping; Yang, Fan; Huang, Yuanyuan

    2015-01-01

    In this paper, some families of nonbinary quantum convolutional codes are constructed by using negacyclic codes. These nonbinary quantum convolutional codes are different from quantum convolutional codes in the literature. Moreover, we construct a family of optimal quantum convolutional codes.

  2. Some easily analyzable convolutional codes

    NASA Technical Reports Server (NTRS)

    Mceliece, R.; Dolinar, S.; Pollara, F.; Vantilborg, H.

    1989-01-01

    Convolutional codes have played and will play a key role in the downlink telemetry systems on many NASA deep-space probes, including Voyager, Magellan, and Galileo. One of the chief difficulties associated with the use of convolutional codes, however, is the notorious difficulty of analyzing them. Given a convolutional code as specified, say, by its generator polynomials, it is no easy matter to say how well that code will perform on a given noisy channel. The usual first step in such an analysis is to computer the code's free distance; this can be done with an algorithm whose complexity is exponential in the code's constraint length. The second step is often to calculate the transfer function in one, two, or three variables, or at least a few terms in its power series expansion. This step is quite hard, and for many codes of relatively short constraint lengths, it can be intractable. However, a large class of convolutional codes were discovered for which the free distance can be computed by inspection, and for which there is a closed-form expression for the three-variable transfer function. Although for large constraint lengths, these codes have relatively low rates, they are nevertheless interesting and potentially useful. Furthermore, the ideas developed here to analyze these specialized codes may well extend to a much larger class.

  3. Convolutional virtual electric field for image segmentation using active contours.

    PubMed

    Wang, Yuanquan; Zhu, Ce; Zhang, Jiawan; Jian, Yuden

    2014-01-01

    Gradient vector flow (GVF) is an effective external force for active contours; however, it suffers from heavy computation load. The virtual electric field (VEF) model, which can be implemented in real time using fast Fourier transform (FFT), has been proposed later as a remedy for the GVF model. In this work, we present an extension of the VEF model, which is referred to as CONvolutional Virtual Electric Field, CONVEF for short. This proposed CONVEF model takes the VEF model as a convolution operation and employs a modified distance in the convolution kernel. The CONVEF model is also closely related to the vector field convolution (VFC) model. Compared with the GVF, VEF and VFC models, the CONVEF model possesses not only some desirable properties of these models, such as enlarged capture range, u-shape concavity convergence, subject contour convergence and initialization insensitivity, but also some other interesting properties such as G-shape concavity convergence, neighboring objects separation, and noise suppression and simultaneously weak edge preserving. Meanwhile, the CONVEF model can also be implemented in real-time by using FFT. Experimental results illustrate these advantages of the CONVEF model on both synthetic and natural images. PMID:25360586

  4. Fabrication of a membrane filter with controlled pore shape and its application to cell separation and strong single cell trapping

    NASA Astrophysics Data System (ADS)

    Choi, Dong-Hoon; Yoon, Gun-Wook; Park, Jeong Won; Ihm, Chunhwa; Lee, Dae-Sik; Yoon, Jun-Bo

    2015-10-01

    A porous membrane filter is one of the key components for sample preparation in lab-on-a-chip applications. However, most of the membranes reported to date have only been used for size-based separation since it is difficult to provide functionality to the membrane or improve the performance of the membrane. In this work, as a method to functionalize the membrane filter, controlling the shape of the membrane pores is suggested, and a convenient and mass-producible fabrication method is provided. With the proposed method, membrane filters with round, conical and funnel shape pores were successfully fabricated, and we demonstrated that the sidewall slope of the conical shape pores could be precisely controlled. To verify that the membrane filter can be functionalized by controlled pore shape, we investigated filtration and trapping performance of the membrane filter with conical shape pores. In a filtration test of 1000 cancer cells (MCF-7, a breast cancer cell line) spiked in phosphate buffered saline (PBS) solution, 77% of the total cancer cells were retained on the membrane, and each cell from among 99.3% of the retained cells was automatically isolated in a single conical pore during the filtration process. Thanks to its engineered pore shape, trapping ability of the membrane with conical pores is dramatically improved. Microparticles trapped in the conical pores maintain their locations without any losses even at a more than 30 times faster external flow rate com-pared with those mounted on conventional cylindrical pores. Also, 78% of the cells trapped in the conical pores withstand an external flow of over 300 μl min-1 whereas only 18% of the cells trapped in the cylindrical pores remain on the membrane after 120 μl min-1 of an external flow is applied.

  5. Fabrication and Characterization of a Micromachined Swirl-Shaped Ionic Polymer Metal Composite Actuator with Electrodes Exhibiting Asymmetric Resistance

    PubMed Central

    Feng, Guo-Hua; Liu, Kim-Min

    2014-01-01

    This paper presents a swirl-shaped microfeatured ionic polymer-metal composite (IPMC) actuator. A novel micromachining process was developed to fabricate an array of IPMC actuators on a glass substrate and to ensure that no shortcircuits occur between the electrodes of the actuator. We demonstrated a microfluidic scheme in which surface tension was used to construct swirl-shaped planar IPMC devices of microfeature size and investigated the flow velocity of Nafion solutions, which formed the backbone polymer of the actuator, within the microchannel. The unique fabrication process yielded top and bottom electrodes that exhibited asymmetric surface resistance. A tool for measuring surface resistance was developed and used to characterize the resistances of the electrodes for the fabricated IPMC device. The actuator, which featured asymmetric electrode resistance, caused a nonzero-bias current when the device was driven using a zero-bias square wave, and we propose a circuit model to describe this phenomenon. Moreover, we discovered and characterized a bending and rotating motion when the IPMC actuator was driven using a square wave. We observed a strain rate of 14.6% and a displacement of 700 μm in the direction perpendicular to the electrode surfaces during 4.5-V actuation. PMID:24824370

  6. Multiple beam interference lithography: A tool for rapid fabrication of plasmonic arrays of arbitrary shaped nanomotifs.

    PubMed

    Vala, M; Homola, J

    2016-07-11

    A novel method enabling rapid fabrication of 2D periodic arrays of plasmonic nanoparticles across large areas is presented. This method is based on the interference of multiple coherent beams originating from diffraction of large-diameter collimated beam on a transmission phase mask. Mutual orientation of the interfering beams is determined by parameters of the used phase mask. Herein, parameters of the phase mask (periods and modulation depth) are selected to yield an interference pattern with high contrast and narrow well-separated maxima. Finally, multiple beam interference lithography (MBIL)-based fabrication of periodic plasmonic arrays with selected nanomotifs including discs, disc dimers, rods and bowtie antennas is demonstrated. PMID:27410838

  7. Nanoscale nickel-titanium shape memory alloys thin films fabricated by using biased target ion beam deposition

    NASA Astrophysics Data System (ADS)

    Hou, Huilong

    Shape memory alloys offer the highest work output per unit volume among smart materials and have both high actuation stress and large recoverable strain. Miniaturization of materials and devices requires shape memory actuation which is uncompromised at a small scale. However, size effects need to be understood in order to scale shape memory actuation with the minimum size critical to device design. Controlling material quality and properties is essential in fabrication of shape memory alloys into nanometer regime. This work demonstrates a novel fabrication technique, biased target ion beam deposition (BTIBD), which uses additional adatom energy in order to fabricate high-quality nickel-titanium (NiTi) alloys thin films with nanometer thickness. These fabricated ultrathin NiTi films provide insight into the size scale dependence of shape memory functionality at nanoscale regime. BTIBD provides additional adatom energy to the growing film in order to fundamentally tailor the film growth mode for quality and properties. An independent ion beam source is customized in BTIBD to provide low-energy ions (tens of eV) during growth of films on substrates. Pure Ti and pure Ni targets are co-sputtering in BTIBD to fabricate NiTi thin films. The prepared NiTi films are continuous, and the thickness ranges from several tens to a few hundreds nanometers. The composition is controllable over the range of Ni-rich (>50.5 at% Ni), near-equiatomic, and Ti-rich (<49.5 at% Ni). The film surfaces are consistently ultra-smooth --- twice as smooth as conventional NiTi thin films fabricated by magnetron sputtering --- over all the composition ranges and over wide surface areas. The substrate/film interface is smooth and the interfacial diffusion is a minimal portion of the film thickness. Crystallographic phases and grain size in BTIBD NiTi films with thickness on the order of 100 nm are tunable via heat treatment. The as-deposited BTIBD films are amorphous. A pure B2 phase (without other

  8. Approximating large convolutions in digital images.

    PubMed

    Mount, D M; Kanungo, T; Netanyahu, N S; Piatko, C; Silverman, R; Wu, A Y

    2001-01-01

    Computing discrete two-dimensional (2-D) convolutions is an important problem in image processing. In mathematical morphology, an important variant is that of computing binary convolutions, where the kernel of the convolution is a 0-1 valued function. This operation can be quite costly, especially when large kernels are involved. We present an algorithm for computing convolutions of this form, where the kernel of the binary convolution is derived from a convex polygon. Because the kernel is a geometric object, we allow the algorithm some flexibility in how it elects to digitize the convex kernel at each placement, as long as the digitization satisfies certain reasonable requirements. We say that such a convolution is valid. Given this flexibility we show that it is possible to compute binary convolutions more efficiently than would normally be possible for large kernels. Our main result is an algorithm which, given an m x n image and a k-sided convex polygonal kernel K, computes a valid convolution in O(kmn) time. Unlike standard algorithms for computing correlations and convolutions, the running time is independent of the area or perimeter of K, and our techniques do not rely on computing fast Fourier transforms. Our algorithm is based on a novel use of Bresenham's (1965) line-drawing algorithm and prefix-sums to update the convolution incrementally as the kernel is moved from one position to another across the image. PMID:18255522

  9. Fabrication of volcano-shaped nano-patterned sapphire substrates using colloidal self-assembly and wet chemical etching.

    PubMed

    Geng, Chong; Zheng, Lu; Fang, Huajing; Yan, Qingfeng; Wei, Tongbo; Hao, Zhibiao; Wang, Xiaoqing; Shen, Dezhong

    2013-08-23

    Patterned sapphire substrates (PSS) have been widely used to enhance the light output power in GaN-based light emitting diodes. The shape and feature size of the pattern in a PSS affect its enhancement efficiency to a great degree. In this work we demonstrate the nanoscale fabrication of volcano-shaped PSS using a wet chemical etching approach in combination with a colloidal monolayer templating strategy. Detailed analysis by scanning electron microscopy reveals that the unique pattern shape is a result of the different corrosion-resistant abilities of silica masks of different effective heights during wet chemical etching. The formation of silica etching masks of different effective heights has been ascribed to the silica precursor solution in the interstice of the colloidal monolayer template being distributed unevenly after infiltration. In the subsequent wet chemical etching process, the active reaction sites altered as etching duration was prolonged, resulting in the formation of volcano-shaped nano-patterned sapphire substrates. PMID:23881090

  10. Shape assisted fabrication of fluorescent cages of squarate based metal-organic coordination frameworks.

    PubMed

    Jayaramulu, Kolleboyina; Krishna, Katla Sai; George, Subi J; Eswaramoorthy, Muthuswamy; Maji, Tapas Kumar

    2013-05-11

    Micronic cage structures of squarate based metal-organic coordination frameworks (MOCFs) have been fabricated for the first time by specific anion selective etching of metal squarate cubes. Time and stoichiometry dependent synthesis and the corresponding microscopic studies have provided mechanistic insight into the cage formation. Furthermore, a non-covalent post-synthetic strategy has been adopted to functionalize the micronic cubes or cages with chromophores rendering the resulting hybrids green fluorescent.

  11. Hydrothermal fabrication of octahedral-shaped Fe3O4 nanoparticles and their magnetorheological response

    NASA Astrophysics Data System (ADS)

    Jung, H. S.; Choi, H. J.

    2015-05-01

    Octahedral-shaped Fe3O4 nanoparticles were synthesized in the presence of 1,3-diaminopropane using a hydrothermal method and assessed as a potential magnetorheological (MR) material. Their morphology, crystal structure, and magnetic properties were examined by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and vibrating sample magnetometry, respectively. The MR characteristics of the octahedral-shaped, Fe3O4 nanoparticle-based MR particles when dispersed in silicone oil with a 10 vol. % particle concentration were examined using a rotational rheometer under an external magnetic field. The resulting MR fluids exhibited a Bingham-like behavior with a distinctive yield stress from their flow curves.

  12. Properties of Porous TiNbZr Shape Memory Alloy Fabricated by Mechanical Alloying and Hot Isostatic Pressing

    NASA Astrophysics Data System (ADS)

    Ma, L. W.; Chung, C. Y.; Tong, Y. X.; Zheng, Y. F.

    2011-07-01

    In the past decades, systematic researches have been focused on studying Ti-Nb-based SMAs by adding ternary elements, such as Mo, Sn, Zr, etc. However, only arc melting or induction melting methods, with subsequent hot or cold rolling, were used to fabricate these Ni-free SMAs. There is no work related to powder metallurgy and porous structures. This study focuses on the fabrication and characterization of porous Ti-22Nb-6Zr (at.%) shape memory alloys produced using elemental powders by means of mechanical alloying and hot isostatic pressing. It is found that the porous Ti-22Nb-6Zr alloys prepared by the HIP process exhibit a homogenous pore distribution with spherical pores, while the pores have irregular shape in the specimen prepared by conventional sintering. X-ray diffraction analysis showed that the solid solution-treated Ti-22Nb-6Zr alloy consists of both β phase and α″ martensite phase. Morphologies of martensite were observed. Finally, the porous Ti-22Nb-6Zr SMAs produced by both MA and HIP exhibit good mechanical properties, such as superior superelasticity, with maximum recoverable strain of ~3% and high compressive strength.

  13. A finger-shaped tactile sensor for fabric surfaces evaluation by 2-dimensional active sliding touch.

    PubMed

    Hu, Haihua; Han, Yezhen; Song, Aiguo; Chen, Shanguang; Wang, Chunhui; Wang, Zheng

    2014-01-01

    Sliding tactile perception is a basic function for human beings to determine the mechanical properties of object surfaces and recognize materials. Imitating this process, this paper proposes a novel finger-shaped tactile sensor based on a thin piezoelectric polyvinylidene fluoride (PVDF) film for surface texture measurement. A parallelogram mechanism is designed to ensure that the sensor applies a constant contact force perpendicular to the object surface, and a 2-dimensional movable mechanical structure is utilized to generate the relative motion at a certain speed between the sensor and the object surface. By controlling the 2-dimensional motion of the finger-shaped sensor along the object surface, small height/depth variation of surface texture changes the output charge of PVDF film then surface texture can be measured. In this paper, the finger-shaped tactile sensor is used to evaluate and classify five different kinds of linen. Fast Fourier Transformation (FFT) is utilized to get original attribute data of surface in the frequency domain, and principal component analysis (PCA) is used to compress the attribute data and extract feature information. Finally, low dimensional features are classified by Support Vector Machine (SVM). The experimental results show that this finger-shaped tactile sensor is effective and high accurate for discriminating the five textures.

  14. A Finger-Shaped Tactile Sensor for Fabric Surfaces Evaluation by 2-Dimensional Active Sliding Touch

    PubMed Central

    Hu, Haihua; Han, Yezhen; Song, Aiguo; Chen, Shanguang; Wang, Chunhui; Wang, Zheng

    2014-01-01

    Sliding tactile perception is a basic function for human beings to determine the mechanical properties of object surfaces and recognize materials. Imitating this process, this paper proposes a novel finger-shaped tactile sensor based on a thin piezoelectric polyvinylidene fluoride (PVDF) film for surface texture measurement. A parallelogram mechanism is designed to ensure that the sensor applies a constant contact force perpendicular to the object surface, and a 2-dimensional movable mechanical structure is utilized to generate the relative motion at a certain speed between the sensor and the object surface. By controlling the 2-dimensional motion of the finger-shaped sensor along the object surface, small height/depth variation of surface texture changes the output charge of PVDF film then surface texture can be measured. In this paper, the finger-shaped tactile sensor is used to evaluate and classify five different kinds of linen. Fast Fourier Transformation (FFT) is utilized to get original attribute data of surface in the frequency domain, and principal component analysis (PCA) is used to compress the attribute data and extract feature information. Finally, low dimensional features are classified by Support Vector Machine (SVM). The experimental results show that this finger-shaped tactile sensor is effective and high accurate for discriminating the five textures. PMID:24618775

  15. Near-net-shape fabrication of continuous Ag-Clad Bi-Based superconductors

    SciTech Connect

    Lanagan, M. T. et al.

    1998-04-01

    We have developed a near-net-shape process for Ag-clad Bi-2212 superconductors as an alternative to the powder-in-tube process. This new process offers the advantages of nearly continuous processing, minimization of processing steps, reasonable ability to control the Bi-2212/Ag ratio, and early development of favorable texture of the Bi-2212 grains. Superconducting properties are discussed.

  16. A finger-shaped tactile sensor for fabric surfaces evaluation by 2-dimensional active sliding touch.

    PubMed

    Hu, Haihua; Han, Yezhen; Song, Aiguo; Chen, Shanguang; Wang, Chunhui; Wang, Zheng

    2014-01-01

    Sliding tactile perception is a basic function for human beings to determine the mechanical properties of object surfaces and recognize materials. Imitating this process, this paper proposes a novel finger-shaped tactile sensor based on a thin piezoelectric polyvinylidene fluoride (PVDF) film for surface texture measurement. A parallelogram mechanism is designed to ensure that the sensor applies a constant contact force perpendicular to the object surface, and a 2-dimensional movable mechanical structure is utilized to generate the relative motion at a certain speed between the sensor and the object surface. By controlling the 2-dimensional motion of the finger-shaped sensor along the object surface, small height/depth variation of surface texture changes the output charge of PVDF film then surface texture can be measured. In this paper, the finger-shaped tactile sensor is used to evaluate and classify five different kinds of linen. Fast Fourier Transformation (FFT) is utilized to get original attribute data of surface in the frequency domain, and principal component analysis (PCA) is used to compress the attribute data and extract feature information. Finally, low dimensional features are classified by Support Vector Machine (SVM). The experimental results show that this finger-shaped tactile sensor is effective and high accurate for discriminating the five textures. PMID:24618775

  17. Crystallographic Fabrics, Grain Boundary Microstructure and Shape Preferred Orientation of Deformed Banded Iron Formations and their Significance for Deformation Interpretation

    NASA Astrophysics Data System (ADS)

    Ávila, Carlos Fernando; Graça, Leonardo; Lagoeiro, Leonardo; Ferreira, Filippe

    2016-04-01

    The characterization of grain boundaries and shapes along with crystallographic preferred orientations (CPOs) are a key aspect of investigations of rock microstructures for their correlation with deformation mechanisms. Rapid developments have occurred in the studying rock microstructures due to recent improvements in analytical techniques such as Electron Backscatter Diffraction (EBSD). EBSD technique allows quick automated microtextural characteritzation. The deformed banded iron formations (BIFs) occurring in the Quadrilátero Ferrífero (QF) province in Brazil have been studied extensively with EBSD. All studies have focused mainly in CPOs. The general agreement is that dislocation creep was the dominant process of deformation, for the strong c-axis fabric of hematite crystals. This idea is substantiated by viscoplastic self-consistent models for deformation of hematite. However there are limitations to analyzing natural CPOs alone, or those generated by deformation models. The strong c-axis fabric could be taken as equally powerful an evidence for other known deformation mechanisms. Some grain boundary types in BIFs of the QF are irregular and comprise equant grains in granoblastic texture (Figure 1a). CPOs for this kind are strong and consistent with a predominance of dislocation creep. Others are very regular and long parallel to basal planes of hematites forming large elongated crystals (lepidoblastic texture, Figure 1b). Such crystals are called specularite, and their formation has been previously attributed to dislocation creep. This is erroneous because of the high strains which would be required. Their shape must be due to anisotropic grain growth. Other types lie between the above end-textures. Both types of grain shape microstructures have the same core deformation mechanism. Describing their genetic differences is crucial, since specularite owe its shape to anisotropic grain growth. It is not possible yet to confirm that dislocation creep was the

  18. Crystallographic Fabrics, Grain Boundary Microstructure and Shape Preferred Orientation of Deformed Banded Iron Formations and their Significance for Deformation Interpretation

    NASA Astrophysics Data System (ADS)

    Avila, C. F.; Lagoeiro, L. E., Sr.; Ferreira, F. O.; Graça, L. M.

    2015-12-01

    The characterization of grain boundaries and shapes along with crystallographic preferred orientations (CPOs) are a key aspect of investigations of rock microstructures for their correlation with deformation mechanisms. Rapid developments have occurred in the studying rock microstructures due to recent improvements in analytical techniques such as Electron Backscatter Diffraction (EBSD). EBSD technique allows quick automated microtextural characteritzation. The deformed banded iron formations (BIFs) occurring in the Quadrilátero Ferrífero (QF) province in Brazil have been studied extensively with EBSD. All studies have focused mainly in CPOs. The general agreement is that dislocation creep was the dominant process of deformation, for the strong c-axis fabric of hematite crystals. This idea is substantiated by viscoplastic self-consistent models for deformation of hematite. However there are limitations to analyzing natural CPOs alone, or those generated by deformation models. The strong c-axis fabric could be taken as equally powerful an evidence for other known deformation mechanisms. Some grain boundary types in BIFs of the QF are irregular and comprise equant grains in granoblastic texture (Figure 1a). CPOs for this kind are strong and consistent with a predominance of dislocation creep. Others are very regular and long parallel to basal planes of hematites forming large elongated crystals (lepidoblastic texture, Figure 1b). Such crystals are called specularite, and their formation has been previously attributed to dislocation creep. This is erroneous because of the high strains which would be required. Their shape must be due to anisotropic grain growth. Other types lie between the above end-textures. Both types of grain shape microstructures have the same core deformation mechanism. Describing their genetic differences is crucial, since specularite owe its shape to anisotropic grain growth. It is not possible yet to confirm that dislocation creep was the

  19. A study of shape-dependent partial volume correction in pet imaging using ellipsoidal phantoms fabricated via rapid prototyping

    NASA Astrophysics Data System (ADS)

    Mille, Matthew M.

    Positron emission tomography (PET) with 2-[18F]fluoro-2-deoxy-D-glucose (FDG) is being increasingly recognized as an important tool for quantitative assessment of tumor response because of its ability to capture functional information about the tumor's metabolism. However, despite many advances in PET technology, measurements of tumor radiopharmaceutical uptake in PET are still challenged by issues of accuracy and consistency, thereby compromising the use of PET as a surrogate endpoint in clinical trials. One limiting component of the overall uncertainty in PET is the relatively poor spatial resolution of the images which directly affects the accuracy of the tumor radioactivity measurements. These spatial resolution effects, colloquially known as the partial volume effect (PVE), are a function of the characteristics of the scanner as well as the tumor being imaged. Previous efforts have shown that the PVE depends strongly on the tumor volume and the background-to-tumor activity concentration ratio. The PVE is also suspected to be a function of tumor shape, although to date no systematic study of this effect has been performed. This dissertation seeks to help fill the gap in the current knowledge about the shape-dependence of the PVE by attempting to quantify, through both theoretical calculation and experimental measurement, the magnitude of the shape effect for ellipsoidal tumors. An experimental investigation of the tumor shape effect necessarily requires tumor phantoms of multiple shapes. Hence, a prerequisite for this research was the design and fabrication of hollow tumor phantoms which could be filled uniformly with radioactivity and imaged on a PET scanner. The phantom fabrication was achieved with the aid of stereolithography and included prolate ellipsoids of various axis ratios. The primary experimental method involved filling the tumor phantoms with solutions of 18F whose activity concentrations were known and traceable to primary radioactivity standards

  20. Nanograined Net-Shaped Fabrication of Rhenium Components by EB-PVD

    SciTech Connect

    Singh, Jogender; Wolfe, Douglas E.

    2004-02-04

    Cost-effective net-shaped forming components have brought considerable interest into DoD, NASA and DoE. Electron beam physical vapor deposition (EB-PVD) offers flexibility in forming net-shaped components with tailored microstructure and chemistry. High purity rhenium (Re) components including rhenium-coated graphite balls, Re- plates and tubes have been successfully manufactured by EB-PVD. EB-PVD Re components exhibited sub-micron and nano-sized grains with high hardness and strength as compared to CVD. It is estimated that the cost of Re components manufactured by EB-PVD would be less than the current CVD and powder-HIP Technologies.

  1. Electron Beam Freeform Fabrication (EBF3) for Cost Effective Near-Net Shape Manufacturing

    NASA Technical Reports Server (NTRS)

    Taminger, Karen M.; Hafley, Robert A.

    2006-01-01

    Manufacturing of structural metal parts directly from computer aided design (CAD) data has been investigated by numerous researchers over the past decade. Researchers at NASA Langley Research Center are developing a new solid freeform fabrication process, electron beam freeform fabrication (EBF3), as a rapid metal deposition process that works efficiently with a variety of weldable alloys. EBF3 deposits of 2219 aluminium and Ti-6Al-4V have exhibited a range of grain morphologies depending upon the deposition parameters. These materials have exhibited excellent tensile properties comparable to typical handbook data for wrought plate product after post-processing heat treatments. The EBF3 process is capable of bulk metal deposition at deposition rates in excess of 2500 cubic centimeters per hour (150 in3/hr) or finer detail at lower deposition rates, depending upon the desired application. This process offers the potential for rapidly adding structural details to simpler cast or forged structures rather than the conventional approach of machining large volumes of chips to produce a monolithic metallic structure. Selective addition of metal onto simpler blanks of material can have a significant effect on lead time reduction and lower material and machining costs.

  2. Electron Beam Freeform Fabrication for Cost Effective Near-Net Shape Manufacturing

    NASA Technical Reports Server (NTRS)

    Taminger, Karen M.; Hafley, Robert A.

    2006-01-01

    Manufacturing of structural metal parts directly from computer aided design (CAD) data has been investigated by numerous researchers over the past decade. Researchers at NASA Langley Research Center are developing a new solid freeform fabrication process, electron beam freeform fabrication (EBF3), as a rapid metal deposition process that works efficiently with a variety of weldable alloys. EBF3 deposits of 2219 aluminium and Ti-6Al-4V have exhibited a range of grain morphologies depending upon the deposition parameters. These materials have exhibited excellent tensile properties comparable to typical handbook data for wrought plate product after post-processing heat treatments. The EBF3 process is capable of bulk metal deposition at deposition rates in excess of 2500 cm3/hr (150 in3/hr) or finer detail at lower deposition rates, depending upon the desired application. This process offers the potential for rapidly adding structural details to simpler cast or forged structures rather than the conventional approach of machining large volumes of chips to produce a monolithic metallic structure. Selective addition of metal onto simpler blanks of material can have a significant effect on lead time reduction and lower material and machining costs.

  3. Fabrication of anatomically-shaped cartilage constructs using decellularized cartilage-derived matrix scaffolds.

    PubMed

    Rowland, Christopher R; Colucci, Lina A; Guilak, Farshid

    2016-06-01

    The native extracellular matrix of cartilage contains entrapped growth factors as well as tissue-specific epitopes for cell-matrix interactions, which make it a potentially attractive biomaterial for cartilage tissue engineering. A limitation to this approach is that the native cartilage extracellular matrix possesses a pore size of only a few nanometers, which inhibits cellular infiltration. Efforts to increase the pore size of cartilage-derived matrix (CDM) scaffolds dramatically attenuate their mechanical properties, which makes them susceptible to cell-mediated contraction. In previous studies, we have demonstrated that collagen crosslinking techniques are capable of preventing cell-mediated contraction in CDM disks. In the current study, we investigated the effects of CDM concentration and pore architecture on the ability of CDM scaffolds to resist cell-mediated contraction. Increasing CDM concentration significantly increased scaffold mechanical properties, which played an important role in preventing contraction, and only the highest CDM concentration (11% w/w) was able to retain the original scaffold dimensions. However, the increase in CDM concentration led to a concomitant decrease in porosity and pore size. Generating a temperature gradient during the freezing process resulted in unidirectional freezing, which aligned the formation of ice crystals during the freezing process and in turn produced aligned pores in CDM scaffolds. These aligned pores increased the pore size of CDM scaffolds at all CDM concentrations, and greatly facilitated infiltration by mesenchymal stem cells (MSCs). These methods were used to fabricate of anatomically-relevant CDM hemispheres. CDM hemispheres with aligned pores supported uniform MSC infiltration and matrix deposition. Furthermore, these CDM hemispheres retained their original architecture and did not contract, warp, curl, or splay throughout the entire 28-day culture period. These findings demonstrate that given the

  4. Top-Down Particle Fabrication: Control of Size and Shape for Diagnostic Imaging and Drug Delivery

    PubMed Central

    Canelas, Dorian A.; Herlihy, Kevin P.; DeSimone, Joseph M.

    2009-01-01

    This review discusses rational design of particles for use as therapeutic vectors and diagnostic imaging agent carriers. The emerging importance of both particle size and shape is considered, and the adaptation and modification of soft lithography methods to produce nanoparticles is highlighted. To this end, studies utilizing particles made via a process called Particle Replication In Non-wetting Templates (PRINT™) are discussed. In addition, insights gained into therapeutic cargo and imaging agent delivery from related types of polymer-based carriers are considered. PMID:20049805

  5. On the growth and form of cortical convolutions

    NASA Astrophysics Data System (ADS)

    Tallinen, Tuomas; Chung, Jun Young; Rousseau, François; Girard, Nadine; Lefèvre, Julien; Mahadevan, L.

    2016-06-01

    The rapid growth of the human cortex during development is accompanied by the folding of the brain into a highly convoluted structure. Recent studies have focused on the genetic and cellular regulation of cortical growth, but understanding the formation of the gyral and sulcal convolutions also requires consideration of the geometry and physical shaping of the growing brain. To study this, we use magnetic resonance images to build a 3D-printed layered gel mimic of the developing smooth fetal brain; when immersed in a solvent, the outer layer swells relative to the core, mimicking cortical growth. This relative growth puts the outer layer into mechanical compression and leads to sulci and gyri similar to those in fetal brains. Starting with the same initial geometry, we also build numerical simulations of the brain modelled as a soft tissue with a growing cortex, and show that this also produces the characteristic patterns of convolutions over a realistic developmental course. All together, our results show that although many molecular determinants control the tangential expansion of the cortex, the size, shape, placement and orientation of the folds arise through iterations and variations of an elementary mechanical instability modulated by early fetal brain geometry.

  6. The trellis complexity of convolutional codes

    NASA Technical Reports Server (NTRS)

    Mceliece, R. J.; Lin, W.

    1995-01-01

    It has long been known that convolutional codes have a natural, regular trellis structure that facilitates the implementation of Viterbi's algorithm. It has gradually become apparent that linear block codes also have a natural, though not in general a regular, 'minimal' trellis structure, which allows them to be decoded with a Viterbi-like algorithm. In both cases, the complexity of the Viterbi decoding algorithm can be accurately estimated by the number of trellis edges per encoded bit. It would, therefore, appear that we are in a good position to make a fair comparison of the Viterbi decoding complexity of block and convolutional codes. Unfortunately, however, this comparison is somewhat muddled by the fact that some convolutional codes, the punctured convolutional codes, are known to have trellis representations that are significantly less complex than the conventional trellis. In other words, the conventional trellis representation for a convolutional code may not be the minimal trellis representation. Thus, ironically, at present we seem to know more about the minimal trellis representation for block than for convolutional codes. In this article, we provide a remedy, by developing a theory of minimal trellises for convolutional codes. (A similar theory has recently been given by Sidorenko and Zyablov). This allows us to make a direct performance-complexity comparison for block and convolutional codes. A by-product of our work is an algorithm for choosing, from among all generator matrices for a given convolutional code, what we call a trellis-minimal generator matrix, from which the minimal trellis for the code can be directly constructed. Another by-product is that, in the new theory, punctured convolutional codes no longer appear as a special class, but simply as high-rate convolutional codes whose trellis complexity is unexpectedly small.

  7. Snow flake shaped gold nanostructures templated on graphene: an avenue to fabricate novel nano electronic devices

    NASA Astrophysics Data System (ADS)

    Jasuja, Kabeer; Berry, Vikas

    2009-03-01

    Non spherical gold nanoparticles such as rods, multipods, polygons, cubes, stars and branched nanostructures have generated significant research attention in the past few years. Such anisotropic nano structures have been shown to exhibit size and shape dependent properties which are either significantly different or highly pronounced from their spherical counterparts. The unique properties of anisotropic nanostructures (such as localized surface plasmon resonance and surface enhanced fluorescence) make these ideal candidates for a broad range of emerging applications in photonics, opto-electronics, biomedical labeling, sensing and imaging. One of the foremost challenges in utilizing such properties is integrating the anisotropic gold nanostructures into devices which can justifiably tap these properties. Here we demonstrate a simple colloidal synthetic route that results in the formation of snow-flake shaped nanostructures of gold (Au SFs) templated on the nano-sheets of Graphene-oxide(GO). Graphene nanosheets have generated renewed interest in recent years due to their unique 2-dimensional nature and associated electronic, physical and chemical properties. An assembly of Au SFs supported on GO sheets will not only give way to the next generation electronic and optoelectronic nanodevices but will also find wide ranging applications in a number of industrially relevant reactions such as catalysis, fuel cell technology and pollution control.

  8. Texture and Crystal Orientation in Ti-6Al-4V Builds Fabricated by Shaped Metal Deposition

    NASA Astrophysics Data System (ADS)

    Baufeld, Bernd; van der Biest, Omer; Dillien, Steven

    2010-08-01

    The texture and crystal orientation of Ti-6Al-4V components, manufactured by shaped metal deposition (SMD), is investigated. SMD is a novel rapid prototyping tungsten inert gas (TIG) welding technique leading to near-net-shape components. This involves sequential layer by layer deposition with repeated partial melting and heat treatment, which results in epitaxial growth of large elongated prior β grains. This leads to a directionally solidified texture, where the prior β grains exhibit only a small misorientation with each other. The β grains grow in left< { 100} rightrangle direction with a second left< { 100} rightrangle direction perpendicular to the wall surface. During cooling, the α phase transformation follows the Burgers orientation relationship leading to a Widmanstätten structure, with orientation relations between most of the α lamellae and also of the residual β phase. The directionally solidification and the transformation into the α phase following the Burgers relationship results in a texture, where the hcp pole figures look similar to bcc pole figures.

  9. Convolution-deconvolution in DIGES

    SciTech Connect

    Philippacopoulos, A.J.; Simos, N.

    1995-05-01

    Convolution and deconvolution operations is by all means a very important aspect of SSI analysis since it influences the input to the seismic analysis. This paper documents some of the convolution/deconvolution procedures which have been implemented into the DIGES code. The 1-D propagation of shear and dilatational waves in typical layered configurations involving a stack of layers overlying a rock is treated by DIGES in a similar fashion to that of available codes, e.g. CARES, SHAKE. For certain configurations, however, there is no need to perform such analyses since the corresponding solutions can be obtained in analytic form. Typical cases involve deposits which can be modeled by a uniform halfspace or simple layered halfspaces. For such cases DIGES uses closed-form solutions. These solutions are given for one as well as two dimensional deconvolution. The type of waves considered include P, SV and SH waves. The non-vertical incidence is given special attention since deconvolution can be defined differently depending on the problem of interest. For all wave cases considered, corresponding transfer functions are presented in closed-form. Transient solutions are obtained in the frequency domain. Finally, a variety of forms are considered for representing the free field motion both in terms of deterministic as well as probabilistic representations. These include (a) acceleration time histories, (b) response spectra (c) Fourier spectra and (d) cross-spectral densities.

  10. Properties of grain boundaries in bulk, melt processed Y-Ba-Cu-O fabricated using bridge-shaped seeds

    NASA Astrophysics Data System (ADS)

    Shi, Y.-H.; Durrell, J. H.; Dennis, A. R.; Babu, N. Hari; Mancini, C. E.; Cardwell, D. A.

    2012-04-01

    Single grain RE-Ba-Cu-O ((RE)BCO, where RE is a rare earth element or yttrium) bulk superconducting materials have significant potential for a variety of engineering applications due to their ability to trap high magnetic fields. However, it is well known that the presence of grain boundaries coupled with a high angle of misorientation (typically 5°) significantly reduces the critical current density, Jc, in all forms of high temperature superconducting materials. It is of considerable fundamental and technological interest, therefore, to investigate the grain boundary properties of bulk, film and tape (RE)BCO. We report a successful multi-seeding technique for the fabrication of fully aligned, artificial (0° misalignment) grain boundaries within large grain YBCO bulk superconductors using bridge-shaped seeds. The microstructure and critical current densities of the grain boundaries produced by this technique have been studied in detail.

  11. Net Shaped Component Fabrication of Refractory Metal Alloys using Vacuum Plasma Spraying

    NASA Technical Reports Server (NTRS)

    Sen, S.; ODell, S.; Gorti, S.; Litchford, R.

    2006-01-01

    The vacuum plasma spraying (VPS) technique was employed to produce dense and net shaped components of a new tungsten-rhenium (W-Re) refractory metal alloy. The fine grain size obtained using this technique enhanced the mechanical properties of the alloy at elevated temperatures. The alloy development also included incorporation of thermodynamically stable dispersion phases to pin down grain boundaries at elevated temperatures and thereby circumventing the inherent problem of recrystallization of refractory alloys at elevated temperatures. Requirements for such alloys as related to high temperature space propulsion components will be discussed. Grain size distribution as a function of cooling rate and dispersion phase loading will be presented. Mechanical testing and grain growth results as a function of temperature will also be discussed.

  12. Experimental considerations on fabrication of a smart actuator for vibration control using shape memory alloy (SMA)

    NASA Astrophysics Data System (ADS)

    Yuse, Kaori; Kikushima, Yoshihiro; Xu, Ya

    2002-06-01

    Despite its great potentials, having a large displacement and force compared to traditional electro-hydraulic servo mechanical actuators or to PZT actuators, there are not so many studies on SMA active actuator. The main reasons are considered as following; (1) SMA has transformation only in one direction, (2) the response is quite slow, and (3) vibration control requires punctual thermo control in real time. In the study at our laboratory, the vibration can be clearly separated into different modes by distributed cluster system. SMA actuators are, then, proposed to use with PZT actuators for control of low and high frequency modes, respectively, to realize all-round actuation. The purpose of this paper is to realize SMA active actuator for low frequency modes. First of all, actuators using SMA wires, partly embedded in CFRP, were fabricated in consideration of SMA/FRP interfacial strength. Their thermo-mechanical behavior had been studied with cooling system. These lightweight actuators were placed on beam structure made of CFRP. Recovery force of beam structure itself was used as reactive force against force generated by SMA. As a result, actuator which is favorable for low frequency vibration modes control, i.e. having a large displacement and a large force, was obtained.

  13. Low-cost methods for fabrication of aluminum-boron shapes

    NASA Technical Reports Server (NTRS)

    Divecha, A. P.; Lalacona, F.

    1974-01-01

    Deformation processing has not been seriously considered as applicable to composite fabrication, particularly if the filament (e.g., boron) lacks plasticity. When, however, the brittle filament is clad with a ductile matrix such as 6061 Al, the underlying filament is protected from damage even when cold worked severely as in drawing and rolling. These two metal-working processes have been applied successfully to produce seven ply aluminum 50 vol % boron tubes up to 2 in. O.D. by 3 feet in length and flats 1 in. in width by 2 feet in length respectively. Although adequate densification is achieved via drawing and rolling, the former has been given major emphasis. Mechanical properties of rolled and drawn flats and tubes compare favorably with those prepared by static methods. However, the objective of the presentation is to focus on lower processing costs. In specific terms, 2 in. tubes in lengths up to 12 feet can be produced for as little as $150/lb. This is possible only because standard drawbench and carbide dies can be utilized. Furthermore, the major steps including cladding, preform preparation and drawing are conducive to automation.

  14. The general theory of convolutional codes

    NASA Technical Reports Server (NTRS)

    Mceliece, R. J.; Stanley, R. P.

    1993-01-01

    This article presents a self-contained introduction to the algebraic theory of convolutional codes. This introduction is partly a tutorial, but at the same time contains a number of new results which will prove useful for designers of advanced telecommunication systems. Among the new concepts introduced here are the Hilbert series for a convolutional code and the class of compact codes.

  15. Achieving unequal error protection with convolutional codes

    NASA Technical Reports Server (NTRS)

    Mills, D. G.; Costello, D. J., Jr.; Palazzo, R., Jr.

    1994-01-01

    This paper examines the unequal error protection capabilities of convolutional codes. Both time-invariant and periodically time-varying convolutional encoders are examined. The effective free distance vector is defined and is shown to be useful in determining the unequal error protection (UEP) capabilities of convolutional codes. A modified transfer function is used to determine an upper bound on the bit error probabilities for individual input bit positions in a convolutional encoder. The bound is heavily dependent on the individual effective free distance of the input bit position. A bound relating two individual effective free distances is presented. The bound is a useful tool in determining the maximum possible disparity in individual effective free distances of encoders of specified rate and memory distribution. The unequal error protection capabilities of convolutional encoders of several rates and memory distributions are determined and discussed.

  16. Search for optimal distance spectrum convolutional codes

    NASA Technical Reports Server (NTRS)

    Connor, Matthew C.; Perez, Lance C.; Costello, Daniel J., Jr.

    1993-01-01

    In order to communicate reliably and to reduce the required transmitter power, NASA uses coded communication systems on most of their deep space satellites and probes (e.g. Pioneer, Voyager, Galileo, and the TDRSS network). These communication systems use binary convolutional codes. Better codes make the system more reliable and require less transmitter power. However, there are no good construction techniques for convolutional codes. Thus, to find good convolutional codes requires an exhaustive search over the ensemble of all possible codes. In this paper, an efficient convolutional code search algorithm was implemented on an IBM RS6000 Model 580. The combination of algorithm efficiency and computational power enabled us to find, for the first time, the optimal rate 1/2, memory 14, convolutional code.

  17. Design and Fabrication of a Biodegradable, Covalently Crosslinked Shape-Memory Alginate Scaffold for Cell and Growth Factor Delivery

    PubMed Central

    Wang, Lin; Shansky, Janet; Borselli, Cristina; Mooney, David

    2012-01-01

    The successful use of transplanted cells and/or growth factors for tissue repair is limited by a significant cell loss and/or rapid growth factor diffusion soon after implantation. Highly porous alginate scaffolds formed with covalent crosslinking have been used to improve cell survival and growth factor release kinetics, but require open-wound surgical procedures for insertion and have not previously been designed to readily degrade in vivo. In this study, a biodegradable, partially crosslinked alginate scaffold with shape-memory properties was fabricated for minimally invasive surgical applications. A mixture of high and low molecular weight partially oxidized alginate modified with RGD peptides was covalently crosslinked using carbodiimide chemistry. The scaffold was compressible 11-fold and returned to its original shape when rehydrated. Scaffold degradation properties in vitro indicated ∼85% mass loss by 28 days. The greater than 90% porous scaffolds released the recombinant growth factor insulin-like growth factor-1 over several days in vitro and allowed skeletal muscle cell survival, proliferation, and migration from the scaffold over a 28-day period. The compressible scaffold thus has the potential to be delivered by a minimally invasive technique, and when rehydrated in vivo with cells and/or growth factors, could serve as a temporary delivery vehicle for tissue repair. PMID:22646518

  18. Fabrication, characterization and some applications of graded chiral zigzag shaped nano-sculptured silver thin films

    NASA Astrophysics Data System (ADS)

    Savaloni, Hadi; Esfandiar, Ali

    2011-09-01

    Graded chiral zig-zag shaped nano-sculptured silver thin films (GCZSSTF) were produced in two stages using oblique deposition technique together with rotation of substrate about its surface normal while a shadowing block was also fixed at the center of the substrate holder. Chrystallographic and morphological structure of these films were obtained using X-ray diffraction (XRD) and atomic force microscopy (AFM). Spectrophotometry was used to obtain their optical behavior while their application in both hydrophobicity and gas sensing was also investigated. XRD results showed a dominant (1 1 1) orientation growth on the zig arm of the structure while by addition of the second arm (zag) the crystallographical growth orientation changed to (2 2 0). The anisotropic nano-structure of these films was also distinguished through (1 - R) spectra. A common peak at about 350 nm related to the TM mode of plasmon resonances and a broad shoulder at about 420 nm for the s-polarized light and at 620 nm for the p-polarized light corresponding to the LM mode of plasmon resonances are observed. These peaks are directly related to the nano-columns topography. The film system used here proved to act as a physical method for producing layer-by-layer structure for obtaining enhanced hydrophobic surfaces rather than the usual chemical methods reported in the literature. In addition, the GCZSSTF also acted as good as reported results for nano-tubes when applied as cathode in the field ionization gas sensing setup.

  19. Easy and cheap fabrication of ordered pyramidal-shaped plasmonic substrates for detection and quantitative analysis using surface-enhanced Raman spectroscopy.

    PubMed

    Leordean, Cosmin; Gabudean, Ana-Maria; Canpean, Valentin; Astilean, Simion

    2013-09-01

    In this work we present a simple approach for the fabrication of periodically ordered pyramidal-shaped metallic nanostructures and demonstrate their efficiency as SERS active substrates. Our method for the fabrication of the plasmonic substrate is based on nanoimprint lithography and exploits the thermal properties of two classes of polymers, thermoplastics and hydrogels. During the heating process the thermoplastic polymers will start to melt whereas the hydrogel polymers will form a solid due to the evaporation of water molecules adsorbed during the dissolving process. Using this approach we fabricate highly ordered pyramidal-shaped nanostructures using the texture of a commercial DVD as the initial mold. This technique represents a low-cost alternative to the classical lithography techniques, allowing the fabrication over large areas (~cm(2)) of periodically ordered nanostructures in a controlled and reproducible manner. The SERS efficiency of the fabricated substrate is demonstrated through the detection of urea molecules found in the fingerprint. In addition, due to the periodicity of the pyramidal-shaped structures, the fabricated substrate can be successfully employed to correlate the intensity of the specific SERS peak of urea with the molecules concentration, offering thus the possibility of developing a quantitative SERS renal sensor.

  20. Cost-Benefit Analysis for the Advanced Near Net Shape Technology (ANNST) Method for Fabricating Stiffened Cylinders

    NASA Technical Reports Server (NTRS)

    Stoner, Mary Cecilia; Hehir, Austin R.; Ivanco, Marie L.; Domack, Marcia S.

    2016-01-01

    This cost-benefit analysis assesses the benefits of the Advanced Near Net Shape Technology (ANNST) manufacturing process for fabricating integrally stiffened cylinders. These preliminary, rough order-of-magnitude results report a 46 to 58 percent reduction in production costs and a 7-percent reduction in weight over the conventional metallic manufacturing technique used in this study for comparison. Production cost savings of 35 to 58 percent were reported over the composite manufacturing technique used in this study for comparison; however, the ANNST concept was heavier. In this study, the predicted return on investment of equipment required for the ANNST method was ten cryogenic tank barrels when compared with conventional metallic manufacturing. The ANNST method was compared with the conventional multi-piece metallic construction and composite processes for fabricating integrally stiffened cylinders. A case study compared these three alternatives for manufacturing a cylinder of specified geometry, with particular focus placed on production costs and process complexity, with cost analyses performed by the analogy and parametric methods. Furthermore, a scalability study was conducted for three tank diameters to assess the highest potential payoff of the ANNST process for manufacture of large-diameter cryogenic tanks. The analytical hierarchy process (AHP) was subsequently used with a group of selected subject matter experts to assess the value of the various benefits achieved by the ANNST method for potential stakeholders. The AHP study results revealed that decreased final cylinder mass and quality assurance were the most valued benefits of cylinder manufacturing methods, therefore emphasizing the relevance of the benefits achieved with the ANNST process for future projects.

  1. Fabrication of three-dimensional porous scaffolds of complicated shape for tissue engineering. I. Compression molding based on flexible-rigid combined mold.

    PubMed

    Wu, Linbo; Zhang, Hong; Zhang, Junchuan; Ding, Jiandong

    2005-01-01

    A novel method for the fabrication of complexly shaped three-dimensional porous scaffolds has been developed by combining modified compression molding and conventional particulate leaching. The resultant scaffolds of various shapes, including some shaped like auricles, were made of hydrophobic biodegradable and bioresorbable poly(D,L-lactic acid) (PDLLA) and poly(D,L-lactic-co-glycolic acid) (PLGA). A polymer-particulate mixture was first prepared by the conventional solvent casting method and then compressively molded in a specially designed flexible-rigid combined mold which facilitates shaping and mold release during the fabrication process. The molding was carried out at a moderate temperature, above the glass transition temperature and below the flow temperature of these amorphous polymers. A porous scaffold was then obtained after particulate leaching. The pores are highly interconnected and uniformly distributed both in the bulk and on the external surface of the scaffolds, and the porosity can exceed 90%. The mechanical properties of the resultant porous scaffolds are satisfactory as determined by measurements of compressive modulus and compressive stress at 10% strain. Good viability of cells seeded in the porous scaffolds was confirmed. This novel fabrication method is promising in tissue engineering because of its ability to produce precise and complexly (anatomically) shaped porous scaffolds.

  2. Design and fabrication of 3D-printed anatomically shaped lumbar cage for intervertebral disc (IVD) degeneration treatment.

    PubMed

    Serra, T; Capelli, C; Toumpaniari, R; Orriss, I R; Leong, J J H; Dalgarno, K; Kalaskar, D M

    2016-01-01

    Spinal fusion is the gold standard surgical procedure for degenerative spinal conditions when conservative therapies have been unsuccessful in rehabilitation of patients. Novel strategies are required to improve biocompatibility and osseointegration of traditionally used materials for lumbar cages. Furthermore, new design and technologies are needed to bridge the gap due to the shortage of optimal implant sizes to fill the intervertebral disc defect. Within this context, additive manufacturing technology presents an excellent opportunity to fabricate ergonomic shape medical implants. The goal of this study is to design and manufacture a 3D-printed lumbar cage for lumbar interbody fusion. Optimisations of the proposed implant design and its printing parameters were achieved via in silico analysis. The final construct was characterised via scanning electron microscopy, contact angle, x-ray micro computed tomography (μCT), atomic force microscopy, and compressive test. Preliminary in vitro cell culture tests such as morphological assessment and metabolic activities were performed to access biocompatibility of 3D-printed constructs. Results of in silico analysis provided a useful platform to test preliminary cage design and to find an optimal value of filling density for 3D printing process. Surface characterisation confirmed a uniform coating of nHAp with nanoscale topography. Mechanical evaluation showed mechanical properties of final cage design similar to that of trabecular bone. Preliminary cell culture results showed promising results in terms of cell growth and activity confirming biocompatibility of constructs. Thus for the first time, design optimisation based on computational and experimental analysis combined with the 3D-printing technique for intervertebral fusion cage has been reported in a single study. 3D-printing is a promising technique for medical applications and this study paves the way for future development of customised implants in spinal

  3. Rapid fabrication of SERS substrate and superhydrophobic surface with different micro/nano-structures by electrochemical shaping of smooth Cu surface

    NASA Astrophysics Data System (ADS)

    Guo, Manman; Liu, Meili; Zhao, Wei; Xia, Yue; Huang, Wei; Li, Zelin

    2015-10-01

    Direct electrochemical shaping of metal surfaces into micro/nano-structures with desired functions is interesting and attractive. In this work, we employed square wave potential pulses (SWPP) to shape a smooth Cu surface into micro/nano-structures efficiently in a blank H2SO4 solution. Delightedly, we obtained Cu sub-micrometric islands on the surface with very strong surface enhanced Raman scattering (SERS) effect in 5 s, and fabricated a coral-like micro/nano-structured copper film with superhydrophobicity in 40 s. This method is green, facile, fast, and easy to control.

  4. Fabrication of transparent, tough, and conductive shape-memory polyurethane films by incorporating a small amount of high-quality graphene.

    PubMed

    Jung, Yong Chae; Kim, Jin Hee; Hayashi, Takuya; Kim, Yoong Ahm; Endo, Morinobu; Terrones, Mauricio; Dresselhaus, Mildred S

    2012-04-23

    We report a mechanically strong, electrically and thermally conductive, and optically transparent shape-memory polyurethane composite which was fabricated by introducing a small amount (0.1 wt%) of high-quality graphene as a filler. Geometrically large (≈4.6 μm(2)), but highly crystallized few-layer graphenes, verified by Raman spectroscopy and transmission electron microscopy, were prepared by the sonication of expandable graphite in an organic solvent. Oxygen- containing functional groups at the edge plane of graphene were crucial for an effective stress transfer from the graphene to polyurethane. Homogeneously dispersed few-layered graphene enabled polyurethane to have a high shape recovery force of 1.8 MPa cm(-3). Graphene, which is intrinsically stretchable up to 10%, will enable high-performance composites to be fabricated at relatively low cost and we thus envisage that such composites may replace carbon nanotubes for various applications in the near future. PMID:22328293

  5. Dip TIPS as a Facile and Versatile Method for Fabrication of Polymer Foams with Controlled Shape, Size and Pore Architecture for Bioengineering Applications

    PubMed Central

    Kasoju, Naresh; Kubies, Dana; Kumorek, Marta M.; Kříž, Jan; Fábryová, Eva; Machová, Lud'ka; Kovářová, Jana; Rypáček, František

    2014-01-01

    The porous polymer foams act as a template for neotissuegenesis in tissue engineering, and, as a reservoir for cell transplants such as pancreatic islets while simultaneously providing a functional interface with the host body. The fabrication of foams with the controlled shape, size and pore structure is of prime importance in various bioengineering applications. To this end, here we demonstrate a thermally induced phase separation (TIPS) based facile process for the fabrication of polymer foams with a controlled architecture. The setup comprises of a metallic template bar (T), a metallic conducting block (C) and a non-metallic reservoir tube (R), connected in sequence T-C-R. The process hereinafter termed as Dip TIPS, involves the dipping of the T-bar into a polymer solution, followed by filling of the R-tube with a freezing mixture to induce the phase separation of a polymer solution in the immediate vicinity of T-bar; Subsequent free-drying or freeze-extraction steps produced the polymer foams. An easy exchange of the T-bar of a spherical or rectangular shape allowed the fabrication of tubular, open- capsular and flat-sheet shaped foams. A mere change in the quenching time produced the foams with a thickness ranging from hundreds of microns to several millimeters. And, the pore size was conveniently controlled by varying either the polymer concentration or the quenching temperature. Subsequent in vivo studies in brown Norway rats for 4-weeks demonstrated the guided cell infiltration and homogenous cell distribution through the polymer matrix, without any fibrous capsule and necrotic core. In conclusion, the results show the “Dip TIPS” as a facile and adaptable process for the fabrication of anisotropic channeled porous polymer foams of various shapes and sizes for potential applications in tissue engineering, cell transplantation and other related fields. PMID:25275373

  6. Astronomical Image Subtraction by Cross-Convolution

    NASA Astrophysics Data System (ADS)

    Yuan, Fang; Akerlof, Carl W.

    2008-04-01

    In recent years, there has been a proliferation of wide-field sky surveys to search for a variety of transient objects. Using relatively short focal lengths, the optics of these systems produce undersampled stellar images often marred by a variety of aberrations. As participants in such activities, we have developed a new algorithm for image subtraction that no longer requires high-quality reference images for comparison. The computational efficiency is comparable with similar procedures currently in use. The general technique is cross-convolution: two convolution kernels are generated to make a test image and a reference image separately transform to match as closely as possible. In analogy to the optimization technique for generating smoothing splines, the inclusion of an rms width penalty term constrains the diffusion of stellar images. In addition, by evaluating the convolution kernels on uniformly spaced subimages across the total area, these routines can accommodate point-spread functions that vary considerably across the focal plane.

  7. Molecular graph convolutions: moving beyond fingerprints.

    PubMed

    Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

    2016-08-01

    Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement. PMID:27558503

  8. Molecular graph convolutions: moving beyond fingerprints.

    PubMed

    Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

    2016-08-01

    Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

  9. Molecular graph convolutions: moving beyond fingerprints

    NASA Astrophysics Data System (ADS)

    Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

    2016-08-01

    Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular "graph convolutions", a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph---atoms, bonds, distances, etc.---which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

  10. Fabrication and characterization of a foamed polylactic acid (PLA)/ thermoplastic polyurethane (TPU) shape memory polymer (SMP) blend for biomedical and clinical applications

    NASA Astrophysics Data System (ADS)

    Song, Janice J.; Srivastava, Ijya; Kowalski, Jennifer; Naguib, Hani E.

    2014-03-01

    Shape memory polymers (SMP) are a class of stimuli-responsive materials that are able to respond to external stimulus such as heat by altering their shape. Bio-compatible SMPs have a number of advantages over static materials and are being studied extensively for biomedical and clinical applications (such as tissue stents and scaffolds). A previous study has demonstrated that the bio-compatible polymer blend of polylactic acid (PLA)/ thermoplastic polyurethane (TPU) (50/50 and 70/30) exhibit good shape memory properties. In this study, the mechanical and thermo-mechanical (shape memory) properties of TPU/PLA SMP blends were characterized; the compositions studied were 80/20, 65/35, and 50/50 TPU/PLA. In addition, porous TPU/PLA SMP blends were fabricated with a gas-foaming technique; and the morphology of the porous structure of these SMPs foams were characterized with scanning electron microscopy (SEM). The TPU/PLA bio-compatible SMP blend was fabricated with melt-blending and compression molding. The glass transition temperature (Tg) of the SMP blends was determined with a differential scanning calorimeter (DSC). The mechanical properties studied were the stress-strain behavior, tensile strength, and elastic modulus; and the thermomechanical (or shape memory) properties studied were the shape fixity rate (Rf), shape recovery rate (Rr), response time, and the effect of recovery temperature on Rr. The porous 80/20 PLA/TPU SMP blend was found to have the highest tensile strength, toughness and percentage extension, as well as the lowest density and uniform pore structure in the micron and submicron scale. The porous 80/20 TPU/PLA SMP blend may be further developed for specific biomedical and clinical applications where a combination of tensile strength, toughness, and low density are required.

  11. Recent Advances in Near-Net-Shape Fabrication of Al-Li Alloy 2195 for Launch Vehicles

    NASA Technical Reports Server (NTRS)

    Wagner, John; Domack, Marcia; Hoffman, Eric

    2007-01-01

    Recent applications in launch vehicles use 2195 processed to Super Lightweight Tank specifications. Potential benefits exist by tailoring heat treatment and other processing parameters to the application. Assess the potential benefits and advocate application of Al-Li near-net-shape technologies for other launch vehicle structural components. Work with manufacturing and material producers to optimize Al-Li ingot shape and size for enhanced near-net-shape processing. Examine time dependent properties of 2195 critical for reusable applications.

  12. Multiple deep convolutional neural networks averaging for face alignment

    NASA Astrophysics Data System (ADS)

    Zhang, Shaohua; Yang, Hua; Yin, Zhouping

    2015-05-01

    Face alignment is critical for face recognition, and the deep learning-based method shows promise for solving such issues, given that competitive results are achieved on benchmarks with additional benefits, such as dispensing with handcrafted features and initial shape. However, most existing deep learning-based approaches are complicated and quite time-consuming during training. We propose a compact face alignment method for fast training without decreasing its accuracy. Rectified linear unit is employed, which allows all networks approximately five times faster convergence than a tanh neuron. An eight learnable layer deep convolutional neural network (DCNN) based on local response normalization and a padding convolutional layer (PCL) is designed to provide reliable initial values during prediction. A model combination scheme is presented to further reduce errors, while showing that only two network architectures and hyperparameter selection procedures are required in our approach. A three-level cascaded system is ultimately built based on the DCNNs and model combination mode. Extensive experiments validate the effectiveness of our method and demonstrate comparable accuracy with state-of-the-art methods on BioID, labeled face parts in the wild, and Helen datasets.

  13. Sequential Syndrome Decoding of Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1984-01-01

    The algebraic structure of convolutional codes are reviewed and sequential syndrome decoding is applied to those codes. These concepts are then used to realize by example actual sequential decoding, using the stack algorithm. The Fano metric for use in sequential decoding is modified so that it can be utilized to sequentially find the minimum weight error sequence.

  14. A Double Precision High Speed Convolution Processor

    NASA Astrophysics Data System (ADS)

    Larochelle, F.; Coté, J. F.; Malowany, A. S.

    1989-11-01

    There exist several convolution processors on the market that can process images at video rate. However, none of these processors operates in floating point arithmetic. Unfortunately, many image processing algorithms presently under development are inoperable in integer arithmetic, forcing the researchers to use regular computers. To solve this problem, we designed a specialized convolution processor that operates in double precision floating point arithmetic with a throughput several thousand times faster than the one obtained on regular computer. Its high performance is attributed to a VLSI double precision convolution systolic cell designed in our laboratories. A 9X9 systolic array carries out, in a pipeline manner, every arithmetic operation. The processor is designed to interface directly with the VME Bus. A DMA chip is responsible for bringing the original pixel intensities from the memory of the computer to the systolic array and to return the convolved pixels back to memory. A special use of 8K RAMs allows an inexpensive and efficient way of delaying the pixel intensities in order to supply the right sequence to the systolic array. On board circuitry converts pixel values into floating point representation when the image is originally represented with integer values. An additional systolic cell, used as a pipeline adder at the output of the systolic array, offers the possibility of combining images together which allows a variable convolution window size and color image processing.

  15. Convolutions and Their Applications in Information Science.

    ERIC Educational Resources Information Center

    Rousseau, Ronald

    1998-01-01

    Presents definitions of convolutions, mathematical operations between sequences or between functions, and gives examples of their use in information science. In particular they can be used to explain the decline in the use of older literature (obsolescence) or the influence of publication delays on the aging of scientific literature. (Author/LRW)

  16. Number-Theoretic Functions via Convolution Rings.

    ERIC Educational Resources Information Center

    Berberian, S. K.

    1992-01-01

    Demonstrates the number theory property that the number of divisors of an integer n times the number of positive integers k, less than or equal to and relatively prime to n, equals the sum of the divisors of n using theory developed about multiplicative functions, the units of a convolution ring, and the Mobius Function. (MDH)

  17. Fabrication of ordered arrays of micro- and nanoscale features with control over their shape and size via templated solid-state dewetting

    PubMed Central

    Ye, Jongpil

    2015-01-01

    Templated solid-state dewetting of single-crystal films has been shown to be used to produce regular patterns of various shapes. However, the materials for which this patterning method is applicable, and the size range of the patterns produced are still limited. Here, it is shown that ordered arrays of micro- and nanoscale features can be produced with control over their shape and size via solid-state dewetting of patches patterned from single-crystal palladium and nickel films of different thicknesses and orientations. The shape and size characteristics of the patterns are found to be widely controllable with varying the shape, width, thickness, and orientation of the initial patches. The morphological evolution of the patches is also dependent on the film material, with different dewetting behaviors observed in palladium and nickel films. The mechanisms underlying the pattern formation are explained in terms of the influence on Rayleigh-like instability of the patch geometry and the surface energy anisotropy of the film material. This mechanistic understanding of pattern formation can be used to design patches for the precise fabrication of micro- and nanoscale structures with the desired shapes and feature sizes. PMID:25951816

  18. Fabrication of metrology test structures for future technology nodes using high-resolution variable-shaped e-beam direct write

    NASA Astrophysics Data System (ADS)

    Szikszai, László; Jaschinsky, Philipp; Keil, Katja; Hauptmann, Marc; Mört, Manfred; Seifert, Uwe; Hohle, Christoph; Choi, Kang-Hoon; Thrum, Frank; Kretz, Johannes; Ferreras Paz, Vaeriano; den Boef, Arie

    2009-03-01

    Electron beam direct write (EBDW) can be utilized for developing metrology methods for future technology nodes. Due to its advantage of high resolution and flexibility combined with suitable throughput capability, variable-shaped E-Beam lithography is the appropriate method to fabricate sub 40nm resist structures with accurately defined properties, such as critical dimension (CD), pitch, line edge roughness (LER) and line width roughness (LWR). In this study we present results of exposure experiments intended to serve as an important instrument for testing and fitting various metrology and defect density measurement methods for future technology nodes. We successfully fabricated sub 40nm gratings with varying CD, pitch, programmed defects and LER/LWR. First metrology measurements by means of optical scatterometry on these dense structures show that variation of the signal response is sufficient to detect sub 10nm fluctuations with a satisfying repeatability.

  19. Deep Learning with Hierarchical Convolutional Factor Analysis

    PubMed Central

    Chen, Bo; Polatkan, Gungor; Sapiro, Guillermo; Blei, David; Dunson, David; Carin, Lawrence

    2013-01-01

    Unsupervised multi-layered (“deep”) models are considered for general data, with a particular focus on imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The computation of layer-dependent model parameters is implemented within a Bayesian setting, employing a Gibbs sampler and variational Bayesian (VB) analysis, that explicitly exploit the convolutional nature of the expansion. In order to address large-scale and streaming data, an online version of VB is also developed. The number of basis functions or dictionary elements at each layer is inferred from the data, based on a beta-Bernoulli implementation of the Indian buffet process. Example results are presented for several image-processing applications, with comparisons to related models in the literature. PMID:23787342

  20. A convolutional neural network neutrino event classifier

    DOE PAGESBeta

    Aurisano, A.; Radovic, A.; Rocco, D.; Himmel, A.; Messier, M. D.; Niner, E.; Pawloski, G.; Psihas, F.; Sousa, A.; Vahle, P.

    2016-09-01

    Here, convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology withoutmore » the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.« less

  1. A convolutional neural network neutrino event classifier

    NASA Astrophysics Data System (ADS)

    Aurisano, A.; Radovic, A.; Rocco, D.; Himmel, A.; Messier, M. D.; Niner, E.; Pawloski, G.; Psihas, F.; Sousa, A.; Vahle, P.

    2016-09-01

    Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  2. A Construction of MDS Quantum Convolutional Codes

    NASA Astrophysics Data System (ADS)

    Zhang, Guanghui; Chen, Bocong; Li, Liangchen

    2015-09-01

    In this paper, two new families of MDS quantum convolutional codes are constructed. The first one can be regarded as a generalization of [36, Theorem 6.5], in the sense that we do not assume that q≡1 (mod 4). More specifically, we obtain two classes of MDS quantum convolutional codes with parameters: (i) [( q 2+1, q 2-4 i+3,1;2,2 i+2)] q , where q≥5 is an odd prime power and 2≤ i≤( q-1)/2; (ii) , where q is an odd prime power with the form q=10 m+3 or 10 m+7 ( m≥2), and 2≤ i≤2 m-1.

  3. Cell differentiation on disk- and string-shaped hydrogels fabricated from Ca(2+) -responsive self-assembling peptides.

    PubMed

    Fukunaga, Kazuto; Tsutsumi, Hiroshi; Mihara, Hisakazu

    2016-11-01

    We recently developed a self-assembling peptide, E1Y9, that self-assembles into nanofibers and forms a hydrogel in the presence of Ca(2+) . E1Y9 derivatives conjugated with functional peptide sequences derived from extracellular matrices (ECMs) reportedly self-assemble into peptide nanofibers that enhance cell adhesion and differentiation. In this study, E1Y9/E1Y9-IKVAV-mixed hydrogels were constructed to serve as artificial ECMs that promote cell differentiation. E1Y9 and E1Y9-IKVAV co-assembled into networked nanofibers, and hydrogels with disk and string shapes were formed in response to Ca(2+) treatment. The neuronal differentiation of PC12 cells was facilitated on hydrogels of both shapes that contained the IKVAV motifs. Moreover, long neurites extended along the long axis of the string-shaped gel, suggesting that the structure of hydrogels of this shape can affect cellular orientation. Thus, E1Y9 hydrogels can potentially be used as artificial ECMs with desirable bioactivities and shapes that could be useful in tissue engineering applications. © 2015 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 476-483, 2016.

  4. Quantum convolutional codes derived from constacyclic codes

    NASA Astrophysics Data System (ADS)

    Yan, Tingsu; Huang, Xinmei; Tang, Yuansheng

    2014-12-01

    In this paper, three families of quantum convolutional codes are constructed. The first one and the second one can be regarded as a generalization of Theorems 3, 4, 7 and 8 [J. Chen, J. Li, F. Yang and Y. Huang, Int. J. Theor. Phys., doi:10.1007/s10773-014-2214-6 (2014)], in the sense that we drop the constraint q ≡ 1 (mod 4). Furthermore, the second one and the third one attain the quantum generalized Singleton bound.

  5. Design and fabrication of a novel XYθz monolithic micro-positioning stage driven by NiTi shape-memory-alloy actuators

    NASA Astrophysics Data System (ADS)

    AbuZaiter, Alaa; Faris Hikmat, Omer; Nafea, Marwan; Ali, Mohamed Sultan Mohamed

    2016-10-01

    This paper reports a new shape-memory-alloy (SMA) micro-positioning stage. The device has been monolithically micro-machined with a single fabrication step. The design comprises a moving stage that is manipulated by six SMA planar springs actuators to generate movements with three degrees of freedom. The overall design is square in shape and has dimensions of 12 mm × 12 mm × 0.25 mm. Localized thermomechanical training for shape setting of SMA planar springs was performed using electrical current induced heating at restrained condition to individually train each of the six actuators to memorize a predetermined shape. For actuation, each SMA actuator is individually driven using Joule heating induced by an electrical current. The current flow is controlled by an external pulse-width modulation signal. The thermal response and heat distribution were simulated and experimentally verified using infrared imaging. The micro-positioning results indicated maximum stage movements of 1.2 and 1.6 mm along the x- and y-directions, respectively. Rotational movements were also demonstrated with a total range of 20°. The developed micro-positioning device has been successfully used to move a small object for microscopic scanning applications.

  6. Convolutional Neural Network Based dem Super Resolution

    NASA Astrophysics Data System (ADS)

    Chen, Zixuan; Wang, Xuewen; Xu, Zekai; Hou, Wenguang

    2016-06-01

    DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.

  7. Hydrothermal fabrication of octahedral-shaped Fe{sub 3}O{sub 4} nanoparticles and their magnetorheological response

    SciTech Connect

    Jung, H. S.; Choi, H. J.

    2015-05-07

    Octahedral-shaped Fe{sub 3}O{sub 4} nanoparticles were synthesized in the presence of 1,3-diaminopropane using a hydrothermal method and assessed as a potential magnetorheological (MR) material. Their morphology, crystal structure, and magnetic properties were examined by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and vibrating sample magnetometry, respectively. The MR characteristics of the octahedral-shaped, Fe{sub 3}O{sub 4} nanoparticle-based MR particles when dispersed in silicone oil with a 10 vol. % particle concentration were examined using a rotational rheometer under an external magnetic field. The resulting MR fluids exhibited a Bingham-like behavior with a distinctive yield stress from their flow curves.

  8. The analysis of convolutional codes via the extended Smith algorithm

    NASA Technical Reports Server (NTRS)

    Mceliece, R. J.; Onyszchuk, I.

    1993-01-01

    Convolutional codes have been the central part of most error-control systems in deep-space communication for many years. Almost all such applications, however, have used the restricted class of (n,1), also known as 'rate 1/n,' convolutional codes. The more general class of (n,k) convolutional codes contains many potentially useful codes, but their algebraic theory is difficult and has proved to be a stumbling block in the evolution of convolutional coding systems. In this article, the situation is improved by describing a set of practical algorithms for computing certain basic things about a convolutional code (among them the degree, the Forney indices, a minimal generator matrix, and a parity-check matrix), which are usually needed before a system using the code can be built. The approach is based on the classic Forney theory for convolutional codes, together with the extended Smith algorithm for polynomial matrices, which is introduced in this article.

  9. Examination of anticipated chemical shift and shape distortion effect on materials commonly used in prosthetic socket fabrication when measured using MRI: a validation study.

    PubMed

    Safari, Mohammad Reza; Rowe, Philip; Buis, Arjan

    2013-01-01

    The quality of lower-limb prosthetic socket fit is influenced by shape and volume consistency during the residual limb shape-capturing process (i.e., casting). Casting can be quantified with magnetic resonance imaging (MRI) technology. However, chemical shift artifact and image distortion may influence the accuracy of MRI when common socket/casting materials are used. We used a purpose-designed rig to examine seven different materials commonly used in socket fabrication during exposure to MRI. The rig incorporated glass marker tubes filled with water doped with 1 g/L copper sulfate (CS) and 9 plastic sample vials (film containers) to hold the specific material specimens. The specimens were scanned 9 times in different configurations. The absolute mean difference of the glass marker tube length was 1.39 mm (2.98%) (minimum = 0.13 mm [0.30%], maximum = 5.47 mm [14.03%], standard deviation = 0.89 mm). The absolute shift for all materials was <1.7 mm. This was less than the measurement tolerance of +/-2.18 mm based on voxel (three-dimensional pixel) dimensions. The results show that MRI is an accurate and repeatable method for dimensional measurement when using matter containing water. Additionally, silicone and plaster of paris plus 1 g/L CS do not show a significant shape distortion nor do they interfere with the MRI image of the residual limb.

  10. Applications of convolution voltammetry in electroanalytical chemistry.

    PubMed

    Bentley, Cameron L; Bond, Alan M; Hollenkamp, Anthony F; Mahon, Peter J; Zhang, Jie

    2014-02-18

    The robustness of convolution voltammetry for determining accurate values of the diffusivity (D), bulk concentration (C(b)), and stoichiometric number of electrons (n) has been demonstrated by applying the technique to a series of electrode reactions in molecular solvents and room temperature ionic liquids (RTILs). In acetonitrile, the relatively minor contribution of nonfaradaic current facilitates analysis with macrodisk electrodes, thus moderate scan rates can be used without the need to perform background subtraction to quantify the diffusivity of iodide [D = 1.75 (±0.02) × 10(-5) cm(2) s(-1)] in this solvent. In the RTIL 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide, background subtraction is necessary at a macrodisk electrode but can be avoided at a microdisk electrode, thereby simplifying the analytical procedure and allowing the diffusivity of iodide [D = 2.70 (±0.03) × 10(-7) cm(2) s(-1)] to be quantified. Use of a convolutive procedure which simultaneously allows D and nC(b) values to be determined is also demonstrated. Three conditions under which a technique of this kind may be applied are explored and are related to electroactive species which display slow dissolution kinetics, undergo a single multielectron transfer step, or contain multiple noninteracting redox centers using ferrocene in an RTIL, 1,4-dinitro-2,3,5,6-tetramethylbenzene, and an alkynylruthenium trimer, respectively, as examples. The results highlight the advantages of convolution voltammetry over steady-state techniques such as rotating disk electrode voltammetry and microdisk electrode voltammetry, as it is not restricted by the mode of diffusion (planar or radial), hence removing limitations on solvent viscosity, electrode geometry, and voltammetric scan rate.

  11. Manifestation of the shape-memory effect in polyetherurethane cellular plastics, fabric composites, and sandwich structures under microgravity

    NASA Astrophysics Data System (ADS)

    Babaevskii, P. G.; Kozlov, N. A.; Agapov, I. G.; Reznichenko, G. M.; Churilo, N. V.; Churilo, I. V.

    2016-09-01

    The results of experiments that were performed to test the feasibility of creating sandwich structures (consisting of thin-layer sheaths of polymer composites and a cellular polymer core) with the shapememory effect as models of the transformable components of space structures have been given. The data obtained indicate that samples of sandwich structures under microgravity conditions on board the International Space Station have recovered their shape to almost the same degree as under terrestrial conditions, which makes it possible to recommend them for creating components of transformable space structures on their basis.

  12. Bacterial colony counting by Convolutional Neural Networks.

    PubMed

    Ferrari, Alessandro; Lombardi, Stefano; Signoroni, Alberto

    2015-01-01

    Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless fundamental task in microbiology. Computer vision based approaches can increase the efficiency and the reliability of the process, but accurate counting is challenging, due to the high degree of variability of agglomerated colonies. In this paper, we propose a solution which adopts Convolutional Neural Networks (CNN) for counting the number of colonies contained in confluent agglomerates, that scored an overall accuracy of the 92.8% on a large challenging dataset. The proposed CNN-based technique for estimating the cardinality of colony aggregates outperforms traditional image processing approaches, becoming a promising approach to many related applications.

  13. Convolution neural networks for ship type recognition

    NASA Astrophysics Data System (ADS)

    Rainey, Katie; Reeder, John D.; Corelli, Alexander G.

    2016-05-01

    Algorithms to automatically recognize ship type from satellite imagery are desired for numerous maritime applications. This task is difficult, and example imagery accurately labeled with ship type is hard to obtain. Convolutional neural networks (CNNs) have shown promise in image recognition settings, but many of these applications rely on the availability of thousands of example images for training. This work attempts to under- stand for which types of ship recognition tasks CNNs might be well suited. We report the results of baseline experiments applying a CNN to several ship type classification tasks, and discuss many of the considerations that must be made in approaching this problem.

  14. QCDNUM: Fast QCD evolution and convolution

    NASA Astrophysics Data System (ADS)

    Botje, M.

    2011-02-01

    The QCDNUM program numerically solves the evolution equations for parton densities and fragmentation functions in perturbative QCD. Un-polarised parton densities can be evolved up to next-to-next-to-leading order in powers of the strong coupling constant, while polarised densities or fragmentation functions can be evolved up to next-to-leading order. Other types of evolution can be accessed by feeding alternative sets of evolution kernels into the program. A versatile convolution engine provides tools to compute parton luminosities, cross-sections in hadron-hadron scattering, and deep inelastic structure functions in the zero-mass scheme or in generalised mass schemes. Input to these calculations are either the QCDNUM evolved densities, or those read in from an external parton density repository. Included in the software distribution are packages to calculate zero-mass structure functions in un-polarised deep inelastic scattering, and heavy flavour contributions to these structure functions in the fixed flavour number scheme. Program summaryProgram title: QCDNUM version: 17.00 Catalogue identifier: AEHV_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHV_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU Public Licence No. of lines in distributed program, including test data, etc.: 45 736 No. of bytes in distributed program, including test data, etc.: 911 569 Distribution format: tar.gz Programming language: Fortran-77 Computer: All Operating system: All RAM: Typically 3 Mbytes Classification: 11.5 Nature of problem: Evolution of the strong coupling constant and parton densities, up to next-to-next-to-leading order in perturbative QCD. Computation of observable quantities by Mellin convolution of the evolved densities with partonic cross-sections. Solution method: Parametrisation of the parton densities as linear or quadratic splines on a discrete grid, and evolution of the spline

  15. Description of a quantum convolutional code.

    PubMed

    Ollivier, Harold; Tillich, Jean-Pierre

    2003-10-24

    We describe a quantum error correction scheme aimed at protecting a flow of quantum information over long distance communication. It is largely inspired by the theory of classical convolutional codes which are used in similar circumstances in classical communication. The particular example shown here uses the stabilizer formalism. We provide an explicit encoding circuit and its associated error estimation algorithm. The latter gives the most likely error over any memoryless quantum channel, with a complexity growing only linearly with the number of encoded qubits.

  16. Solution-Based Fabrication of Narrow-Disperse ABC Three-Segment and Θ-Shaped Nanoparticles.

    PubMed

    Zhang, Zhen; Zhou, Changming; Dong, Haiyan; Chen, Daoyong

    2016-05-17

    Nanoparticles sized tens of nm with not only a highly complex but also a highly regular nanostructure, although ubiquitous in nature, are very difficult to prepare artificially. Herein, we report efficient solution-based preparation of narrow-disperse ABC three-segment hierarchical nanoparticles (HNPs) with a size of tens of nm through a three-level hierarchical self-assembly of A-b-B-b-C triblock copolymers in solution. An ABC HNP is composed of three nanoparticles, A, B, and C that are linearly connected; in the ABC HNP, the B nanoparticle is sandwiched between the A and C nanoparticles. The method for the preparation is highly efficient, because all of the A-b-B-b-C chains in the solution are converted into the ABC HNPs. Furthermore, the ABC HNPs self-assembled into Θ-shaped HNPs tens nm in size. Both the ABC and Θ-shaped HNPs, are highly complex but highly regular, and are novel HNPs, and they should be very promising for addressing various theoretical and practical problems. PMID:27071692

  17. Flexible Fabrication of Shape-Controlled Collagen Building Blocks for Self-Assembly of 3D Microtissues.

    PubMed

    Zhang, Xu; Meng, Zhaoxu; Ma, Jingyun; Shi, Yang; Xu, Hui; Lykkemark, Simon; Qin, Jianhua

    2015-08-12

    Creating artificial tissue-like structures that possess the functionality, specificity, and architecture of native tissues remains a big challenge. A new and straightforward strategy for generating shape-controlled collagen building blocks with a well-defined architecture is presented, which can be used for self-assembly of complex 3D microtissues. Collagen blocks with tunable geometries are controllably produced and released via a membrane-templated microdevice. The formation of functional microtissues by embedding tissue-specific cells into collagen blocks with expression of specific proteins is described. The spontaneous self-assembly of cell-laden collagen blocks into organized tissue constructs with predetermined configurations is demonstrated, which are largely driven by the synergistic effects of cell-cell and cell-matrix interactions. This new strategy would open up new avenues for the study of tissue/organ morphogenesis, and tissue engineering applications.

  18. Flexible Fabrication of Shape-Controlled Collagen Building Blocks for Self-Assembly of 3D Microtissues.

    PubMed

    Zhang, Xu; Meng, Zhaoxu; Ma, Jingyun; Shi, Yang; Xu, Hui; Lykkemark, Simon; Qin, Jianhua

    2015-08-12

    Creating artificial tissue-like structures that possess the functionality, specificity, and architecture of native tissues remains a big challenge. A new and straightforward strategy for generating shape-controlled collagen building blocks with a well-defined architecture is presented, which can be used for self-assembly of complex 3D microtissues. Collagen blocks with tunable geometries are controllably produced and released via a membrane-templated microdevice. The formation of functional microtissues by embedding tissue-specific cells into collagen blocks with expression of specific proteins is described. The spontaneous self-assembly of cell-laden collagen blocks into organized tissue constructs with predetermined configurations is demonstrated, which are largely driven by the synergistic effects of cell-cell and cell-matrix interactions. This new strategy would open up new avenues for the study of tissue/organ morphogenesis, and tissue engineering applications. PMID:25920010

  19. Convolution formulations for non-negative intensity.

    PubMed

    Williams, Earl G

    2013-08-01

    Previously unknown spatial convolution formulas for a variant of the active normal intensity in planar coordinates have been derived that use measured pressure or normal velocity near-field holograms to construct a positive-only (outward) intensity distribution in the plane, quantifying the areas of the vibrating structure that produce radiation to the far-field. This is an extension of the outgoing-only (unipolar) intensity technique recently developed for arbitrary geometries by Steffen Marburg. The method is applied independently to pressure and velocity data measured in a plane close to the surface of a point-driven, unbaffled rectangular plate in the laboratory. It is demonstrated that the sound producing regions of the structure are clearly revealed using the derived formulas and that the spatial resolution is limited to a half-wavelength. A second set of formulas called the hybrid-intensity formulas are also derived which yield a bipolar intensity using a different spatial convolution operator, again using either the measured pressure or velocity. It is demonstrated from the experiment results that the velocity formula yields the classical active intensity and the pressure formula an interesting hybrid intensity that may be useful for source localization. Computations are fast and carried out in real space without Fourier transforms into wavenumber space. PMID:23927105

  20. SU-E-T-08: A Convolution Model for Head Scatter Fluence in the Intensity Modulated Field

    SciTech Connect

    Chen, M; Mo, X; Chen, Y; Parnell, D; Key, S; Olivera, G; Galmarini, W; Lu, W

    2014-06-01

    Purpose: To efficiently calculate the head scatter fluence for an arbitrary intensity-modulated field with any source distribution using the source occlusion model. Method: The source occlusion model with focal and extra focal radiation (Jaffray et al, 1993) can be used to account for LINAC head scatter. In the model, the fluence map of any field shape at any point can be calculated via integration of the source distribution within the visible range, as confined by each segment, using the detector eye's view. A 2D integration would be required for each segment and each fluence plane point, which is time-consuming, as an intensity-modulated field contains typically tens to hundreds of segments. In this work, we prove that the superposition of the segmental integrations is equivalent to a simple convolution regardless of what the source distribution is. In fact, for each point, the detector eye's view of the field shape can be represented as a function with the origin defined at the point's pinhole reflection through the center of the collimator plane. We were thus able to reduce hundreds of source plane integration to one convolution. We calculated the fluence map for various 3D and IMRT beams and various extra-focal source distributions using both the segmental integration approach and the convolution approach and compared the computation time and fluence map results of both approaches. Results: The fluence maps calculated using the convolution approach were the same as those calculated using the segmental approach, except for rounding errors (<0.1%). While it took considerably longer time to calculate all segmental integrations, the fluence map calculation using the convolution approach took only ∼1/3 of the time for typical IMRT fields with ∼100 segments. Conclusions: The convolution approach for head scatter fluence calculation is fast and accurate and can be used to enhance the online process.

  1. Wafer-scale fabrication of penetrating neural microelectrode arrays

    NASA Astrophysics Data System (ADS)

    Bhandari, Rajmohan

    In order to have an efficient neural interface, uniformity and predictability of electrodes electrical, and mechanical characteristics are desired. Furthermore, the electrodes should have small active sites to selectively record or stimulate neural signals. Also, there should be close geometrical match between the electrode array and the targeted tissue for long-term stability. Currently the Utah electrode array (UEA) is in either constant electrode length (UEA) or varying length configurations (Utah slant electrode array: USEA). The current processes used to fabricate the UEAs impose limitations in the tolerances of the electrode array geometry. Furthermore, the flat architecture of the UEA and convoluted geometry of the targeted tissue results in poor coupling between the two "mating" surfaces, leading in active electrode tips that are not in proximity to the neuronal tissue. Therefore, a robust, flexible and high precision fabrication technology is needed that can produce (a) uniformly shaped microelectrodes (b) small and uniformly exposed active tip sites and (c) convoluted electrode arrays for better geometrical match. This dissertation presents a wafer-scale fabrication process for both the UEA and the USEA. A wafer-scale etching method has been developed and optimum etching conditions are established to achieve uniform shape electrode arrays. Also, the etching rate of silicon columns, produced by dicing, is studied as a function of temperature, etching time and stirring rate in the acid solution. Furthermore, a novel photoresist based masking technique for procuring extremely small active area has been developed on wafer-scale. In this technique, the tip exposure is controlled by varying the spin speed during photoresist coating. The technique allows fabrication of uniformly exposed tip lengths, over a range of 30 to 350 microm in length. Lastly, a novel array fabrication technique is developed for building a variety of neural interface devices having

  2. Experimental Investigation of Convoluted Contouring for Aircraft Afterbody Drag Reduction

    NASA Technical Reports Server (NTRS)

    Deere, Karen A.; Hunter, Craig A.

    1999-01-01

    An experimental investigation was performed in the NASA Langley 16-Foot Transonic Tunnel to determine the aerodynamic effects of external convolutions, placed on the boattail of a nonaxisymmetric nozzle for drag reduction. Boattail angles of 15 and 22 were tested with convolutions placed at a forward location upstream of the boattail curvature, at a mid location along the curvature and at a full location that spanned the entire boattail flap. Each of the baseline nozzle afterbodies (no convolutions) had a parabolic, converging contour with a parabolically decreasing corner radius. Data were obtained at several Mach numbers from static conditions to 1.2 for a range of nozzle pressure ratios and angles of attack. An oil paint flow visualization technique was used to qualitatively assess the effect of the convolutions. Results indicate that afterbody drag reduction by convoluted contouring is convolution location, Mach number, boattail angle, and NPR dependent. The forward convolution location was the most effective contouring geometry for drag reduction on the 22 afterbody, but was only effective for M < 0.95. At M = 0.8, drag was reduced 20 and 36 percent at NPRs of 5.4 and 7, respectively, but drag was increased 10 percent for M = 0.95 at NPR = 7. Convoluted contouring along the 15 boattail angle afterbody was not effective at reducing drag because the flow was minimally separated from the baseline afterbody, unlike the massive separation along the 22 boattail angle baseline afterbody.

  3. Predicting Flow-Induced Vibrations In A Convoluted Hose

    NASA Technical Reports Server (NTRS)

    Harvey, Stuart A.

    1994-01-01

    Composite model constructed from two less accurate models. Predicts approximately frequencies and modes of vibrations induced by flows of various fluids in convoluted hose. Based partly on spring-and-lumped-mass representation of dynamics involving springiness and mass of convolution of hose and density of fluid in hose.

  4. New quantum MDS-convolutional codes derived from constacyclic codes

    NASA Astrophysics Data System (ADS)

    Li, Fengwei; Yue, Qin

    2015-12-01

    In this paper, we utilize a family of Hermitian dual-containing constacyclic codes to construct classical and quantum MDS convolutional codes. Our classical and quantum convolutional codes are optimal in the sense that they attain the classical (quantum) generalized Singleton bound.

  5. Shape alterations of ZnO nanocrystal arrays fabricated from NH 3·H 2O solutions

    NASA Astrophysics Data System (ADS)

    Yu, Ke; Jin, Zhengguo; Liu, Xiaoxin; Zhao, Juan; Feng, Junyi

    2007-02-01

    Well-aligned crystalline ZnO nanorod arrays were synthesized via an aqueous solution route with ammonia and zinc nitrate as inorganic precursors. ZnO crystalline seed films were firstly coated on ITO substrates for epitaxial growth of rods through sol-gel processing and heat treatment. SEM, TEM, SAED and XRD were utilized to characterize morphologies and structures of ZnO crystals. Heterogeneous nucleation is crucial for rod growth. A broad scope of pH favorable for heterogeneous nucleation was disclosed at zinc concentration from 0.04 to 0.1 M in the inorganic system due to the complex reaction of ammonia with Zn 2+. Elevation of initial zinc concentration or pH promoted growth rate of rods and enlarged rod size. ZnO nanorods were transformed to nanotubes, nanosheets and rods with blanket-like shaped surface mainly by secondary pH adjustment. All ZnO nanocrystals are wurtzite structure preferentially oriented in c-axis direction.

  6. Convolutional code performance in planetary entry channels

    NASA Technical Reports Server (NTRS)

    Modestino, J. W.

    1974-01-01

    The planetary entry channel is modeled for communication purposes representing turbulent atmospheric scattering effects. The performance of short and long constraint length convolutional codes is investigated in conjunction with coherent BPSK modulation and Viterbi maximum likelihood decoding. Algorithms for sequential decoding are studied in terms of computation and/or storage requirements as a function of the fading channel parameters. The performance of the coded coherent BPSK system is compared with the coded incoherent MFSK system. Results indicate that: some degree of interleaving is required to combat time correlated fading of channel; only modest amounts of interleaving are required to approach performance of memoryless channel; additional propagational results are required on the phase perturbation process; and the incoherent MFSK system is superior when phase tracking errors are considered.

  7. Image statistics decoding for convolutional codes

    NASA Technical Reports Server (NTRS)

    Pitt, G. H., III; Swanson, L.; Yuen, J. H.

    1987-01-01

    It is a fact that adjacent pixels in a Voyager image are very similar in grey level. This fact can be used in conjunction with the Maximum-Likelihood Convolutional Decoder (MCD) to decrease the error rate when decoding a picture from Voyager. Implementing this idea would require no changes in the Voyager spacecraft and could be used as a backup to the current system without too much expenditure, so the feasibility of it and the possible gains for Voyager were investigated. Simulations have shown that the gain could be as much as 2 dB at certain error rates, and experiments with real data inspired new ideas on ways to get the most information possible out of the received symbol stream.

  8. Multi-modal vertebrae recognition using Transformed Deep Convolution Network.

    PubMed

    Cai, Yunliang; Landis, Mark; Laidley, David T; Kornecki, Anat; Lum, Andrea; Li, Shuo

    2016-07-01

    Automatic vertebra recognition, including the identification of vertebra locations and naming in multiple image modalities, are highly demanded in spinal clinical diagnoses where large amount of imaging data from various of modalities are frequently and interchangeably used. However, the recognition is challenging due to the variations of MR/CT appearances or shape/pose of the vertebrae. In this paper, we propose a method for multi-modal vertebra recognition using a novel deep learning architecture called Transformed Deep Convolution Network (TDCN). This new architecture can unsupervisely fuse image features from different modalities and automatically rectify the pose of vertebra. The fusion of MR and CT image features improves the discriminativity of feature representation and enhances the invariance of the vertebra pattern, which allows us to automatically process images from different contrast, resolution, protocols, even with different sizes and orientations. The feature fusion and pose rectification are naturally incorporated in a multi-layer deep learning network. Experiment results show that our method outperforms existing detection methods and provides a fully automatic location+naming+pose recognition for routine clinical practice. PMID:27104497

  9. Convolution modeling of two-domain, nonlinear water-level responses in karst aquifers (Invited)

    NASA Astrophysics Data System (ADS)

    Long, A. J.

    2009-12-01

    Convolution modeling is a useful method for simulating the hydraulic response of water levels to sinking streamflow or precipitation infiltration at the macro scale. This approach is particularly useful in karst aquifers, where the complex geometry of the conduit and pore network is not well characterized but can be represented approximately by a parametric impulse-response function (IRF) with very few parameters. For many applications, one-dimensional convolution models can be equally effective as complex two- or three-dimensional models for analyzing water-level responses to recharge. Moreover, convolution models are well suited for identifying and characterizing the distinct domains of quick flow and slow flow (e.g., conduit flow and diffuse flow). Two superposed lognormal functions were used in the IRF to approximate the impulses of the two flow domains. Nonlinear response characteristics of the flow domains were assessed by observing temporal changes in the IRFs. Precipitation infiltration was simulated by filtering the daily rainfall record with a backward-in-time exponential function that weights each day’s rainfall with the rainfall of previous days and thus accounts for the effects of soil moisture on aquifer infiltration. The model was applied to the Edwards aquifer in Texas and the Madison aquifer in South Dakota. Simulations of both aquifers showed similar characteristics, including a separation on the order of years between the quick-flow and slow-flow IRF peaks and temporal changes in the IRF shapes when water levels increased and empty pore spaces became saturated.

  10. A novel one-pot process for near-net-shape fabrication of open-porous resorbable hydroxyapatite/protein composites and in vivo assessment.

    PubMed

    Mueller, Berit; Koch, Dietmar; Lutz, Rainer; Schlegel, Karl A; Treccani, Laura; Rezwan, Kurosch

    2014-09-01

    We present a mild one-pot freeze gelation process for fabricating near-net, complex-shaped hydroxyapatite scaffolds and to directly incorporate active proteins during scaffold processing. In particular, the direct protein incorporation enables a simultaneous adjustment and control of scaffold microstructure, porosity, resorbability and enhancement of initial mechanical and handling stability. Two proteins, serum albumin and lysozyme, are selected and their effect on scaffold stability and microstructure investigated by biaxial strength tests, electron microscopy, and mercury intrusion porosimetry. The resulting hydroxyapatite/protein composites feature adjustable porosities from 50% to 70% and a mechanical strength ranging from 2 to 6 MPa comparable to that of human spongiosa without any sintering step. Scaffold degradation behaviour and protein release are assessed by in vitro studies. A preliminary in vivo assessment of scaffold biocompatibility and resorption behaviour in adult domestic pigs is discussed. After implantation, composites were resorbed up to 50% after only 4 weeks and up to 65% after 8 weeks. In addition, 14% new bone formation after 4 weeks and 37% after 8 weeks were detected. All these investigations demonstrate the outstanding suitability of the one-pot-process to create, in a customisable and reliable way, biocompatible scaffolds with sufficient mechanical strength for handling and surgical insertion, and for potential use as biodegradable bone substitutes and versatile platform for local drug delivery.

  11. A novel one-pot process for near-net-shape fabrication of open-porous resorbable hydroxyapatite/protein composites and in vivo assessment.

    PubMed

    Mueller, Berit; Koch, Dietmar; Lutz, Rainer; Schlegel, Karl A; Treccani, Laura; Rezwan, Kurosch

    2014-09-01

    We present a mild one-pot freeze gelation process for fabricating near-net, complex-shaped hydroxyapatite scaffolds and to directly incorporate active proteins during scaffold processing. In particular, the direct protein incorporation enables a simultaneous adjustment and control of scaffold microstructure, porosity, resorbability and enhancement of initial mechanical and handling stability. Two proteins, serum albumin and lysozyme, are selected and their effect on scaffold stability and microstructure investigated by biaxial strength tests, electron microscopy, and mercury intrusion porosimetry. The resulting hydroxyapatite/protein composites feature adjustable porosities from 50% to 70% and a mechanical strength ranging from 2 to 6 MPa comparable to that of human spongiosa without any sintering step. Scaffold degradation behaviour and protein release are assessed by in vitro studies. A preliminary in vivo assessment of scaffold biocompatibility and resorption behaviour in adult domestic pigs is discussed. After implantation, composites were resorbed up to 50% after only 4 weeks and up to 65% after 8 weeks. In addition, 14% new bone formation after 4 weeks and 37% after 8 weeks were detected. All these investigations demonstrate the outstanding suitability of the one-pot-process to create, in a customisable and reliable way, biocompatible scaffolds with sufficient mechanical strength for handling and surgical insertion, and for potential use as biodegradable bone substitutes and versatile platform for local drug delivery. PMID:25063103

  12. Metaheuristic Algorithms for Convolution Neural Network

    PubMed Central

    Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

    2016-01-01

    A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738

  13. Metaheuristic Algorithms for Convolution Neural Network.

    PubMed

    Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

    2016-01-01

    A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738

  14. Convolutional Sparse Coding for Trajectory Reconstruction.

    PubMed

    Zhu, Yingying; Lucey, Simon

    2015-03-01

    Trajectory basis Non-Rigid Structure from Motion (NRSfM) refers to the process of reconstructing the 3D trajectory of each point of a non-rigid object from just their 2D projected trajectories. Reconstruction relies on two factors: (i) the condition of the composed camera & trajectory basis matrix, and (ii) whether the trajectory basis has enough degrees of freedom to model the 3D point trajectory. These two factors are inherently conflicting. Employing a trajectory basis with small capacity has the positive characteristic of reducing the likelihood of an ill-conditioned system (when composed with the camera) during reconstruction. However, this has the negative characteristic of increasing the likelihood that the basis will not be able to fully model the object's "true" 3D point trajectories. In this paper we draw upon a well known result centering around the Reduced Isometry Property (RIP) condition for sparse signal reconstruction. RIP allow us to relax the requirement that the full trajectory basis composed with the camera matrix must be well conditioned. Further, we propose a strategy for learning an over-complete basis using convolutional sparse coding from naturally occurring point trajectory corpora to increase the likelihood that the RIP condition holds for a broad class of point trajectories and camera motions. Finally, we propose an l1 inspired objective for trajectory reconstruction that is able to "adaptively" select the smallest sub-matrix from an over-complete trajectory basis that balances (i) and (ii). We present more practical 3D reconstruction results compared to current state of the art in trajectory basis NRSfM.

  15. Noise-enhanced convolutional neural networks.

    PubMed

    Audhkhasi, Kartik; Osoba, Osonde; Kosko, Bart

    2016-06-01

    Injecting carefully chosen noise can speed convergence in the backpropagation training of a convolutional neural network (CNN). The Noisy CNN algorithm speeds training on average because the backpropagation algorithm is a special case of the generalized expectation-maximization (EM) algorithm and because such carefully chosen noise always speeds up the EM algorithm on average. The CNN framework gives a practical way to learn and recognize images because backpropagation scales with training data. It has only linear time complexity in the number of training samples. The Noisy CNN algorithm finds a special separating hyperplane in the network's noise space. The hyperplane arises from the likelihood-based positivity condition that noise-boosts the EM algorithm. The hyperplane cuts through a uniform-noise hypercube or Gaussian ball in the noise space depending on the type of noise used. Noise chosen from above the hyperplane speeds training on average. Noise chosen from below slows it on average. The algorithm can inject noise anywhere in the multilayered network. Adding noise to the output neurons reduced the average per-iteration training-set cross entropy by 39% on a standard MNIST image test set of handwritten digits. It also reduced the average per-iteration training-set classification error by 47%. Adding noise to the hidden layers can also reduce these performance measures. The noise benefit is most pronounced for smaller data sets because the largest EM hill-climbing gains tend to occur in the first few iterations. This noise effect can assist random sampling from large data sets because it allows a smaller random sample to give the same or better performance than a noiseless sample gives.

  16. Colonoscopic polyp detection using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Park, Sun Young; Sargent, Dusty

    2016-03-01

    Computer aided diagnosis (CAD) systems for medical image analysis rely on accurate and efficient feature extraction methods. Regardless of which type of classifier is used, the results will be limited if the input features are not diagnostically relevant and do not properly discriminate between the different classes of images. Thus, a large amount of research has been dedicated to creating feature sets that capture the salient features that physicians are able to observe in the images. Successful feature extraction reduces the semantic gap between the physician's interpretation and the computer representation of images, and helps to reduce the variability in diagnosis between physicians. Due to the complexity of many medical image classification tasks, feature extraction for each problem often requires domainspecific knowledge and a carefully constructed feature set for the specific type of images being classified. In this paper, we describe a method for automatic diagnostic feature extraction from colonoscopy images that may have general application and require a lower level of domain-specific knowledge. The work in this paper expands on our previous CAD algorithm for detecting polyps in colonoscopy video. In that work, we applied an eigenimage model to extract features representing polyps, normal tissue, diverticula, etc. from colonoscopy videos taken from various viewing angles and imaging conditions. Classification was performed using a conditional random field (CRF) model that accounted for the spatial and temporal adjacency relationships present in colonoscopy video. In this paper, we replace the eigenimage feature descriptor with features extracted from a convolutional neural network (CNN) trained to recognize the same image types in colonoscopy video. The CNN-derived features show greater invariance to viewing angles and image quality factors when compared to the eigenimage model. The CNN features are used as input to the CRF classifier as before. We report

  17. Fourier deconvolution reveals the role of the Lorentz function as the convolution kernel of narrow photon beams

    NASA Astrophysics Data System (ADS)

    Djouguela, Armand; Harder, Dietrich; Kollhoff, Ralf; Foschepoth, Simon; Kunth, Wolfgang; Rühmann, Antje; Willborn, Kay; Poppe, Björn

    2009-05-01

    The two-dimensional lateral dose profiles D(x, y) of narrow photon beams, typically used for beamlet-based IMRT, stereotactic radiosurgery and tomotherapy, can be regarded as resulting from the convolution of a two-dimensional rectangular function R(x, y), which represents the photon fluence profile within the field borders, with a rotation-symmetric convolution kernel K(r). This kernel accounts not only for the lateral transport of secondary electrons and small-angle scattered photons in the absorber, but also for the 'geometrical spread' of each pencil beam due to the phase-space distribution of the photon source. The present investigation of the convolution kernel was based on an experimental study of the associated line-spread function K(x). Systematic cross-plane scans of rectangular and quadratic fields of variable side lengths were made by utilizing the linear current versus dose rate relationship and small energy dependence of the unshielded Si diode PTW 60012 as well as its narrow spatial resolution function. By application of the Fourier convolution theorem, it was observed that the values of the Fourier transform of K(x) could be closely fitted by an exponential function exp(-2πλνx) of the spatial frequency νx. Thereby, the line-spread function K(x) was identified as the Lorentz function K(x) = (λ/π)[1/(x2 + λ2)], a single-parameter, bell-shaped but non-Gaussian function with a narrow core, wide curve tail, full half-width 2λ and convenient convolution properties. The variation of the 'kernel width parameter' λ with the photon energy, field size and thickness of a water-equivalent absorber was systematically studied. The convolution of a rectangular fluence profile with K(x) in the local space results in a simple equation accurately reproducing the measured lateral dose profiles. The underlying 2D convolution kernel (point-spread function) was identified as K(r) = (λ/2π)[1/(r2 + λ2)]3/2, fitting experimental results as well. These results are

  18. Modelling ocean carbon cycle with a nonlinear convolution model

    NASA Astrophysics Data System (ADS)

    Kheshgi, Haroon S.; White, Benjamin S.

    1996-02-01

    A nonlinear convolution integral is developed to model the response of the ocean carbon sink to changes in the atmospheric concentration of CO2. This model can accurately represent the atmospheric response of complex ocean carbon cycle models in which the nonlinear behavior stems from the nonlinear dependence of CO2 solubility in seawater on CO2 partial pressure, which is often represented by the buffer factor. The kernel of the nonlinear convolution model can be constructed from a response of such a complex model to an arbitrary change in CO2 emissions, along with the functional dependence of the buffer factor. Once the convolution kernel has been constructed, either analytically or from a model experiment, the convolution representation can be used to estimate responses of the ocean carbon sink to other changes in the atmospheric concentration of CO2. Thus the method can be used, e.g., to explore alternative emissions scenarios for assessments of climate change. A derivation for the nonlinear convolution integral model is given, and the model is used to reproduce the response of two carbon cycle models: a one-dimensional diffusive ocean model, and a three-dimensional ocean-general-circulation tracer model.

  19. Evaluation of convolutional neural networks for visual recognition.

    PubMed

    Nebauer, C

    1998-01-01

    Convolutional neural networks provide an efficient method to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. This network topology has been applied in particular to image classification when sophisticated preprocessing is to be avoided and raw images are to be classified directly. In this paper two variations of convolutional networks--neocognitron and a modification of neocognitron--are compared with classifiers based on fully connected feedforward layers (i.e., multilayer perceptron, nearest neighbor classifier, auto-encoding network) with respect to their visual recognition performance. Beside the original neocognitron a modification of the neocognitron is proposed which combines neurons from perceptron with the localized network structure of neocognitron. Instead of training convolutional networks by time-consuming error backpropagation, in this work a modular procedure is applied whereby layers are trained sequentially from the input to the output layer in order to recognize features of increasing complexity. For a quantitative experimental comparison with standard classifiers two very different recognition tasks have been chosen: handwritten digit recognition and face recognition. In the first example on handwritten digit recognition the generalization of convolutional networks is compared to fully connected networks. In several experiments the influence of variations of position, size, and orientation of digits is determined and the relation between training sample size and validation error is observed. In the second example recognition of human faces is investigated under constrained and variable conditions with respect to face orientation and illumination and the limitations of convolutional networks are discussed.

  20. Error-trellis Syndrome Decoding Techniques for Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1984-01-01

    An error-trellis syndrome decoding technique for convolutional codes is developed. This algorithm is then applied to the entire class of systematic convolutional codes and to the high-rate, Wyner-Ash convolutional codes. A special example of the one-error-correcting Wyner-Ash code, a rate 3/4 code, is treated. The error-trellis syndrome decoding method applied to this example shows in detail how much more efficient syndrome decoding is than Viterbi decoding if applied to the same problem. For standard Viterbi decoding, 64 states are required, whereas in the example only 7 states are needed. Also, within the 7 states required for decoding, many fewer transitions are needed between the states.

  1. Error-trellis syndrome decoding techniques for convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1985-01-01

    An error-trellis syndrome decoding technique for convolutional codes is developed. This algorithm is then applied to the entire class of systematic convolutional codes and to the high-rate, Wyner-Ash convolutional codes. A special example of the one-error-correcting Wyner-Ash code, a rate 3/4 code, is treated. The error-trellis syndrome decoding method applied to this example shows in detail how much more efficient syndrome decordig is than Viterbi decoding if applied to the same problem. For standard Viterbi decoding, 64 states are required, whereas in the example only 7 states are needed. Also, within the 7 states required for decoding, many fewer transitions are needed between the states.

  2. Glaucoma detection based on deep convolutional neural network.

    PubMed

    Xiangyu Chen; Yanwu Xu; Damon Wing Kee Wong; Tien Yin Wong; Jiang Liu

    2015-08-01

    Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma detection. PMID:26736362

  3. Glaucoma detection based on deep convolutional neural network.

    PubMed

    Xiangyu Chen; Yanwu Xu; Damon Wing Kee Wong; Tien Yin Wong; Jiang Liu

    2015-08-01

    Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma detection.

  4. Two dimensional convolute integers for machine vision and image recognition

    NASA Technical Reports Server (NTRS)

    Edwards, Thomas R.

    1988-01-01

    Machine vision and image recognition require sophisticated image processing prior to the application of Artificial Intelligence. Two Dimensional Convolute Integer Technology is an innovative mathematical approach for addressing machine vision and image recognition. This new technology generates a family of digital operators for addressing optical images and related two dimensional data sets. The operators are regression generated, integer valued, zero phase shifting, convoluting, frequency sensitive, two dimensional low pass, high pass and band pass filters that are mathematically equivalent to surface fitted partial derivatives. These operators are applied non-recursively either as classical convolutions (replacement point values), interstitial point generators (bandwidth broadening or resolution enhancement), or as missing value calculators (compensation for dead array element values). These operators show frequency sensitive feature selection scale invariant properties. Such tasks as boundary/edge enhancement and noise or small size pixel disturbance removal can readily be accomplished. For feature selection tight band pass operators are essential. Results from test cases are given.

  5. Knowledge Based 3d Building Model Recognition Using Convolutional Neural Networks from LIDAR and Aerial Imageries

    NASA Astrophysics Data System (ADS)

    Alidoost, F.; Arefi, H.

    2016-06-01

    In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings' roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings' roofs automatically considering the complementary nature of height and RGB information.

  6. Bioprinting of 3D Convoluted Renal Proximal Tubules on Perfusable Chips

    PubMed Central

    Homan, Kimberly A.; Kolesky, David B.; Skylar-Scott, Mark A.; Herrmann, Jessica; Obuobi, Humphrey; Moisan, Annie; Lewis, Jennifer A.

    2016-01-01

    Three-dimensional models of kidney tissue that recapitulate human responses are needed for drug screening, disease modeling, and, ultimately, kidney organ engineering. Here, we report a bioprinting method for creating 3D human renal proximal tubules in vitro that are fully embedded within an extracellular matrix and housed in perfusable tissue chips, allowing them to be maintained for greater than two months. Their convoluted tubular architecture is circumscribed by proximal tubule epithelial cells and actively perfused through the open lumen. These engineered 3D proximal tubules on chip exhibit significantly enhanced epithelial morphology and functional properties relative to the same cells grown on 2D controls with or without perfusion. Upon introducing the nephrotoxin, Cyclosporine A, the epithelial barrier is disrupted in a dose-dependent manner. Our bioprinting method provides a new route for programmably fabricating advanced human kidney tissue models on demand. PMID:27725720

  7. Bioprinting of 3D Convoluted Renal Proximal Tubules on Perfusable Chips

    NASA Astrophysics Data System (ADS)

    Homan, Kimberly A.; Kolesky, David B.; Skylar-Scott, Mark A.; Herrmann, Jessica; Obuobi, Humphrey; Moisan, Annie; Lewis, Jennifer A.

    2016-10-01

    Three-dimensional models of kidney tissue that recapitulate human responses are needed for drug screening, disease modeling, and, ultimately, kidney organ engineering. Here, we report a bioprinting method for creating 3D human renal proximal tubules in vitro that are fully embedded within an extracellular matrix and housed in perfusable tissue chips, allowing them to be maintained for greater than two months. Their convoluted tubular architecture is circumscribed by proximal tubule epithelial cells and actively perfused through the open lumen. These engineered 3D proximal tubules on chip exhibit significantly enhanced epithelial morphology and functional properties relative to the same cells grown on 2D controls with or without perfusion. Upon introducing the nephrotoxin, Cyclosporine A, the epithelial barrier is disrupted in a dose-dependent manner. Our bioprinting method provides a new route for programmably fabricating advanced human kidney tissue models on demand.

  8. Voltage measurements at the vacuum post-hole convolute of the Z pulsed-power accelerator

    DOE PAGESBeta

    Waisman, E. M.; McBride, R. D.; Cuneo, M. E.; Wenger, D. F.; Fowler, W. E.; Johnson, W. A.; Basilio, L. I.; Coats, R. S.; Jennings, C. A.; Sinars, D. B.; et al

    2014-12-08

    Presented are voltage measurements taken near the load region on the Z pulsed-power accelerator using an inductive voltage monitor (IVM). Specifically, the IVM was connected to, and thus monitored the voltage at, the bottom level of the accelerator’s vacuum double post-hole convolute. Additional voltage and current measurements were taken at the accelerator’s vacuum-insulator stack (at a radius of 1.6 m) by using standard D-dot and B-dot probes, respectively. During postprocessing, the measurements taken at the stack were translated to the location of the IVM measurements by using a lossless propagation model of the Z accelerator’s magnetically insulated transmission lines (MITLs)more » and a lumped inductor model of the vacuum post-hole convolute. Across a wide variety of experiments conducted on the Z accelerator, the voltage histories obtained from the IVM and the lossless propagation technique agree well in overall shape and magnitude. However, large-amplitude, high-frequency oscillations are more pronounced in the IVM records. It is unclear whether these larger oscillations represent true voltage oscillations at the convolute or if they are due to noise pickup and/or transit-time effects and other resonant modes in the IVM. Results using a transit-time-correction technique and Fourier analysis support the latter. Regardless of which interpretation is correct, both true voltage oscillations and the excitement of resonant modes could be the result of transient electrical breakdowns in the post-hole convolute, though more information is required to determine definitively if such breakdowns occurred. Despite the larger oscillations in the IVM records, the general agreement found between the lossless propagation results and the results of the IVM shows that large voltages are transmitted efficiently through the MITLs on Z. These results are complementary to previous studies [R. D. McBride et al., Phys. Rev. ST Accel. Beams 13, 120401 (2010)] that showed

  9. Voltage measurements at the vacuum post-hole convolute of the Z pulsed-power accelerator

    NASA Astrophysics Data System (ADS)

    Waisman, E. M.; McBride, R. D.; Cuneo, M. E.; Wenger, D. F.; Fowler, W. E.; Johnson, W. A.; Basilio, L. I.; Coats, R. S.; Jennings, C. A.; Sinars, D. B.; Vesey, R. A.; Jones, B.; Ampleford, D. J.; Lemke, R. W.; Martin, M. R.; Schrafel, P. C.; Lewis, S. A.; Moore, J. K.; Savage, M. E.; Stygar, W. A.

    2014-12-01

    Presented are voltage measurements taken near the load region on the Z pulsed-power accelerator using an inductive voltage monitor (IVM). Specifically, the IVM was connected to, and thus monitored the voltage at, the bottom level of the accelerator's vacuum double post-hole convolute. Additional voltage and current measurements were taken at the accelerator's vacuum-insulator stack (at a radius of 1.6 m) by using standard D -dot and B -dot probes, respectively. During postprocessing, the measurements taken at the stack were translated to the location of the IVM measurements by using a lossless propagation model of the Z accelerator's magnetically insulated transmission lines (MITLs) and a lumped inductor model of the vacuum post-hole convolute. Across a wide variety of experiments conducted on the Z accelerator, the voltage histories obtained from the IVM and the lossless propagation technique agree well in overall shape and magnitude. However, large-amplitude, high-frequency oscillations are more pronounced in the IVM records. It is unclear whether these larger oscillations represent true voltage oscillations at the convolute or if they are due to noise pickup and/or transit-time effects and other resonant modes in the IVM. Results using a transit-time-correction technique and Fourier analysis support the latter. Regardless of which interpretation is correct, both true voltage oscillations and the excitement of resonant modes could be the result of transient electrical breakdowns in the post-hole convolute, though more information is required to determine definitively if such breakdowns occurred. Despite the larger oscillations in the IVM records, the general agreement found between the lossless propagation results and the results of the IVM shows that large voltages are transmitted efficiently through the MITLs on Z . These results are complementary to previous studies [R. D. McBride et al., Phys. Rev. ST Accel. Beams 13, 120401 (2010)] that showed efficient

  10. Voltage measurements at the vacuum post-hole convolute of the Z pulsed-power accelerator

    SciTech Connect

    Waisman, E. M.; McBride, R. D.; Cuneo, M. E.; Wenger, D. F.; Fowler, W. E.; Johnson, W. A.; Basilio, L. I.; Coats, R. S.; Jennings, C. A.; Sinars, D. B.; Vesey, R. A.; Jones, B.; Ampleford, D. J.; Lemke, R. W.; Martin, M. R.; Schrafel, P. C.; Lewis, S. A.; Moore, J. K.; Savage, M. E.; Stygar, W. A.

    2014-12-08

    Presented are voltage measurements taken near the load region on the Z pulsed-power accelerator using an inductive voltage monitor (IVM). Specifically, the IVM was connected to, and thus monitored the voltage at, the bottom level of the accelerator’s vacuum double post-hole convolute. Additional voltage and current measurements were taken at the accelerator’s vacuum-insulator stack (at a radius of 1.6 m) by using standard D-dot and B-dot probes, respectively. During postprocessing, the measurements taken at the stack were translated to the location of the IVM measurements by using a lossless propagation model of the Z accelerator’s magnetically insulated transmission lines (MITLs) and a lumped inductor model of the vacuum post-hole convolute. Across a wide variety of experiments conducted on the Z accelerator, the voltage histories obtained from the IVM and the lossless propagation technique agree well in overall shape and magnitude. However, large-amplitude, high-frequency oscillations are more pronounced in the IVM records. It is unclear whether these larger oscillations represent true voltage oscillations at the convolute or if they are due to noise pickup and/or transit-time effects and other resonant modes in the IVM. Results using a transit-time-correction technique and Fourier analysis support the latter. Regardless of which interpretation is correct, both true voltage oscillations and the excitement of resonant modes could be the result of transient electrical breakdowns in the post-hole convolute, though more information is required to determine definitively if such breakdowns occurred. Despite the larger oscillations in the IVM records, the general agreement found between the lossless propagation results and the results of the IVM shows that large voltages are transmitted efficiently through the MITLs on Z. These results are complementary to previous studies [R. D. McBride et al., Phys. Rev. ST Accel. Beams 13, 120401 (2010)] that

  11. ARKCoS: artifact-suppressed accelerated radial kernel convolution on the sphere

    NASA Astrophysics Data System (ADS)

    Elsner, F.; Wandelt, B. D.

    2011-08-01

    We describe a hybrid Fourier/direct space convolution algorithm for compact radial (azimuthally symmetric) kernels on the sphere. For high resolution maps covering a large fraction of the sky, our implementation takes advantage of the inexpensive massive parallelism afforded by consumer graphics processing units (GPUs). Its applications include modeling of instrumental beam shapes in terms of compact kernels, computation of fine-scale wavelet transformations, and optimal filtering for the detection of point sources. Our algorithm works for any pixelization where pixels are grouped into isolatitude rings. Even for kernels that are not bandwidth-limited, ringing features are completely absent on an ECP grid. We demonstrate that they can be highly suppressed on the popular HEALPix pixelization, for which we develop a freely available implementation of the algorithm. As an example application, we show that running on a high-end consumer graphics card our method speeds up beam convolution for simulations of a characteristic Planck high frequency instrument channel by two orders of magnitude compared to the commonly used HEALPix implementation on one CPU core, while typically maintaining a fractional RMS accuracy of about 1 part in 105.

  12. Real-time rendering of optical effects using spatial convolution

    NASA Astrophysics Data System (ADS)

    Rokita, Przemyslaw

    1998-03-01

    Simulation of special effects such as: defocus effect, depth-of-field effect, raindrops or water film falling on the windshield, may be very useful in visual simulators and in all computer graphics applications that need realistic images of outdoor scenery. Those effects are especially important in rendering poor visibility conditions in flight and driving simulators, but can also be applied, for example, in composing computer graphics and video sequences- -i.e. in Augmented Reality systems. This paper proposes a new approach to the rendering of those optical effects by iterative adaptive filtering using spatial convolution. The advantage of this solution is that the adaptive convolution can be done in real-time by existing hardware. Optical effects mentioned above can be introduced into the image computed using conventional camera model by applying to the intensity of each pixel the convolution filter having an appropriate point spread function. The algorithms described in this paper can be easily implemented int the visualization pipeline--the final effect may be obtained by iterative filtering using a single hardware convolution filter or with the pipeline composed of identical 3 X 3 filters placed as the stages of this pipeline. Another advantage of the proposed solution is that the extension based on proposed algorithm can be added to the existing rendering systems as a final stage of the visualization pipeline.

  13. Face recognition: a convolutional neural-network approach.

    PubMed

    Lawrence, S; Giles, C L; Tsoi, A C; Back, A D

    1997-01-01

    We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

  14. Reply to 'Comment on 'Quantum convolutional error-correcting codes''

    SciTech Connect

    Chau, H.F.

    2005-08-15

    In their Comment, de Almeida and Palazzo [Phys. Rev. A 72, 026301 (2005)] discovered an error in my earlier paper concerning the construction of quantum convolutional codes [Phys. Rev. A 58, 905 (1998)]. This error can be repaired by modifying the method of code construction.

  15. Maximum-likelihood estimation of circle parameters via convolution.

    PubMed

    Zelniker, Emanuel E; Clarkson, I Vaughan L

    2006-04-01

    The accurate fitting of a circle to noisy measurements of circumferential points is a much studied problem in the literature. In this paper, we present an interpretation of the maximum-likelihood estimator (MLE) and the Delogne-Kåsa estimator (DKE) for circle-center and radius estimation in terms of convolution on an image which is ideal in a certain sense. We use our convolution-based MLE approach to find good estimates for the parameters of a circle in digital images. In digital images, it is then possible to treat these estimates as preliminary estimates into various other numerical techniques which further refine them to achieve subpixel accuracy. We also investigate the relationship between the convolution of an ideal image with a "phase-coded kernel" (PCK) and the MLE. This is related to the "phase-coded annulus" which was introduced by Atherton and Kerbyson who proposed it as one of a number of new convolution kernels for estimating circle center and radius. We show that the PCK is an approximate MLE (AMLE). We compare our AMLE method to the MLE and the DKE as well as the Cramér-Rao Lower Bound in ideal images and in both real and synthetic digital images. PMID:16579374

  16. FAST PIXEL SPACE CONVOLUTION FOR COSMIC MICROWAVE BACKGROUND SURVEYS WITH ASYMMETRIC BEAMS AND COMPLEX SCAN STRATEGIES: FEBeCoP

    SciTech Connect

    Mitra, S.; Rocha, G.; Gorski, K. M.; Lawrence, C. R.; Huffenberger, K. M.; Eriksen, H. K.; Ashdown, M. A. J. E-mail: graca@caltech.edu E-mail: Charles.R.Lawrence@jpl.nasa.gov E-mail: h.k.k.eriksen@astro.uio.no

    2011-03-15

    Precise measurement of the angular power spectrum of the cosmic microwave background (CMB) temperature and polarization anisotropy can tightly constrain many cosmological models and parameters. However, accurate measurements can only be realized in practice provided all major systematic effects have been taken into account. Beam asymmetry, coupled with the scan strategy, is a major source of systematic error in scanning CMB experiments such as Planck, the focus of our current interest. We envision Monte Carlo methods to rigorously study and account for the systematic effect of beams in CMB analysis. Toward that goal, we have developed a fast pixel space convolution method that can simulate sky maps observed by a scanning instrument, taking into account real beam shapes and scan strategy. The essence is to pre-compute the 'effective beams' using a computer code, 'Fast Effective Beam Convolution in Pixel space' (FEBeCoP), that we have developed for the Planck mission. The code computes effective beams given the focal plane beam characteristics of the Planck instrument and the full history of actual satellite pointing, and performs very fast convolution of sky signals using the effective beams. In this paper, we describe the algorithm and the computational scheme that has been implemented. We also outline a few applications of the effective beams in the precision analysis of Planck data, for characterizing the CMB anisotropy and for detecting and measuring properties of point sources.

  17. Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning.

    PubMed

    Song, Youyi; Zhang, Ling; Chen, Siping; Ni, Dong; Lei, Baiying; Wang, Tianfu

    2015-10-01

    In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically, deep learning via the MSCN is explored to extract scale invariant features, and then, segment regions centered at each pixel. The coarse segmentation is refined by an automated graph partitioning method based on the pretrained feature. The texture, shape, and contextual information of the target objects are learned to localize the appearance of distinctive boundary, which is also explored to generate markers to split the touching nuclei. For further refinement of the segmentation, a coarse-to-fine nucleus segmentation framework is developed. The computational complexity of the segmentation is reduced by using superpixel instead of raw pixels. Extensive experimental results demonstrate that the proposed cervical nucleus cell segmentation delivers promising results and outperforms existing methods.

  18. Segmenting delaminations in carbon fiber reinforced polymer composite CT using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Sammons, Daniel; Winfree, William P.; Burke, Eric; Ji, Shuiwang

    2016-02-01

    Nondestructive evaluation (NDE) utilizes a variety of techniques to inspect various materials for defects without causing changes to the material. X-ray computed tomography (CT) produces large volumes of three dimensional image data. Using the task of identifying delaminations in carbon fiber reinforced polymer (CFRP) composite CT, this work shows that it is possible to automate the analysis of these large volumes of CT data using a machine learning model known as a convolutional neural network (CNN). Further, tests on simulated data sets show that with a robust set of experimental data, it may be possible to go beyond just identification and instead accurately characterize the size and shape of the delaminations with CNNs.

  19. Practical implementation of a collapsed cone convolution algorithm for a radiation treatment planning system

    NASA Astrophysics Data System (ADS)

    Cho, Woong; Suh, Tae-Suk; Park, Jeong-Hoon; Xing, Lei; Lee, Jeong-Woo

    2012-12-01

    A collapsed cone convolution algorithm was applied to a treatment planning system for the calculation of dose distributions. The distribution of beam fluences was determined using a three-source model by considering the source strengths of the primary beam, the beam scattered from the primary collimators, and an extra beam scattered from extra structures in the gantry head of the radiotherapy treatment machine. The distribution of the total energy released per unit mass (TERMA) was calculated from the distribution of the fluence by considering several physical effects such as the emission of poly-energetic photon spectra, the attenuation of the beam fluence in a medium, the horn effect, the beam-softening effect, and beam transmission through collimators or multi-leaf collimators. The distribution of the doses was calculated by using the convolution of the distribution of the TERMA and the poly-energetic kernel. The distribution of the kernel was approximated to several tens of collapsed cone lines to express the energies transferred by the electrons that originated from the interactions between the photons and the medium. The implemented algorithm was validated by comparing the calculated percentage depth doses (PDDs) and dose profiles with the measured PDDs and relevant profiles. In addition, the dose distribution for an irregular-shaped radiation field was verified by comparing the calculated doses with the measured doses obtained via EDR2 film dosimetry and with the calculated doses obtained using a different treatment planning system based on the pencil beam algorithm (Eclipse, Varian, Palo Alto, USA). The majority of the calculated doses for the PDDs, the profiles, and the irregular-shaped field showed good agreement with the measured doses to within a 2% dose difference, except in the build-up regions. The implemented algorithm was proven to be efficient and accurate for clinical purposes in radiation therapy, and it was found to be easily implementable in

  20. Photo-crosslinked fabrication of novel biocompatible and elastomeric star-shaped inositol-based polymer with highly tunable mechanical behavior and degradation.

    PubMed

    Xie, Meihua; Ge, Juan; Xue, Yumeng; Du, Yuzhang; Lei, Bo; Ma, Peter X

    2015-11-01

    Biodegradable and star-shaped polymers with highly tunable structure and properties have attracted much attention in recent years for potential biomedical applications, due to their special structure. Here, inositol-based star-shaped poly-L-lactide-poly(ethylene glycol) (INO-PLLA-PEG) biomedical polymer implants were for the first time synthesized by a facile photo-crosslinking method. This biomaterials show controlled elastomeric mechanical properties (~18 MPa in tensile strength, ~200 MPa in modulus, ~200% in elongation), biodegradability and osteoblasts biocompatibility. These results make INO-PLLA-PEG implants highly promising for bone tissue regeneration and drug delivery applications. PMID:26253207

  1. Spectral density of generalized Wishart matrices and free multiplicative convolution

    NASA Astrophysics Data System (ADS)

    Młotkowski, Wojciech; Nowak, Maciej A.; Penson, Karol A.; Życzkowski, Karol

    2015-07-01

    We investigate the level density for several ensembles of positive random matrices of a Wishart-like structure, W =X X† , where X stands for a non-Hermitian random matrix. In particular, making use of the Cauchy transform, we study the free multiplicative powers of the Marchenko-Pastur (MP) distribution, MP⊠s, which for an integer s yield Fuss-Catalan distributions corresponding to a product of s -independent square random matrices, X =X1⋯Xs . New formulas for the level densities are derived for s =3 and s =1 /3 . Moreover, the level density corresponding to the generalized Bures distribution, given by the free convolution of arcsine and MP distributions, is obtained. We also explain the reason of such a curious convolution. The technique proposed here allows for the derivation of the level densities for several other cases.

  2. Spectral density of generalized Wishart matrices and free multiplicative convolution.

    PubMed

    Młotkowski, Wojciech; Nowak, Maciej A; Penson, Karol A; Życzkowski, Karol

    2015-07-01

    We investigate the level density for several ensembles of positive random matrices of a Wishart-like structure, W=XX(†), where X stands for a non-Hermitian random matrix. In particular, making use of the Cauchy transform, we study the free multiplicative powers of the Marchenko-Pastur (MP) distribution, MP(⊠s), which for an integer s yield Fuss-Catalan distributions corresponding to a product of s-independent square random matrices, X=X(1)⋯X(s). New formulas for the level densities are derived for s=3 and s=1/3. Moreover, the level density corresponding to the generalized Bures distribution, given by the free convolution of arcsine and MP distributions, is obtained. We also explain the reason of such a curious convolution. The technique proposed here allows for the derivation of the level densities for several other cases.

  3. a Convolutional Network for Semantic Facade Segmentation and Interpretation

    NASA Astrophysics Data System (ADS)

    Schmitz, Matthias; Mayer, Helmut

    2016-06-01

    In this paper we present an approach for semantic interpretation of facade images based on a Convolutional Network. Our network processes the input images in a fully convolutional way and generates pixel-wise predictions. We show that there is no need for large datasets to train the network when transfer learning is employed, i. e., a part of an already existing network is used and fine-tuned, and when the available data is augmented by using deformed patches of the images for training. The network is trained end-to-end with patches of the images and each patch is augmented independently. To undo the downsampling for the classification, we add deconvolutional layers to the network. Outputs of different layers of the network are combined to achieve more precise pixel-wise predictions. We demonstrate the potential of our network based on results for the eTRIMS (Korč and Förstner, 2009) dataset reduced to facades.

  4. UFLIC: A Line Integral Convolution Algorithm for Visualizing Unsteady Flows

    NASA Technical Reports Server (NTRS)

    Shen, Han-Wei; Kao, David L.; Chancellor, Marisa K. (Technical Monitor)

    1997-01-01

    This paper presents an algorithm, UFLIC (Unsteady Flow LIC), to visualize vector data in unsteady flow fields. Using the Line Integral Convolution (LIC) as the underlying method, a new convolution algorithm is proposed that can effectively trace the flow's global features over time. The new algorithm consists of a time-accurate value depositing scheme and a successive feed-forward method. The value depositing scheme accurately models the flow advection, and the successive feed-forward method maintains the coherence between animation frames. Our new algorithm can produce time-accurate, highly coherent flow animations to highlight global features in unsteady flow fields. CFD scientists, for the first time, are able to visualize unsteady surface flows using our algorithm.

  5. Image Super-Resolution Using Deep Convolutional Networks.

    PubMed

    Dong, Chao; Loy, Chen Change; He, Kaiming; Tang, Xiaoou

    2016-02-01

    We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality. PMID:26761735

  6. Deep learning for steganalysis via convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu

    2015-03-01

    Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.

  7. Image Super-Resolution Using Deep Convolutional Networks.

    PubMed

    Dong, Chao; Loy, Chen Change; He, Kaiming; Tang, Xiaoou

    2016-02-01

    We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.

  8. A new computational decoding complexity measure of convolutional codes

    NASA Astrophysics Data System (ADS)

    Benchimol, Isaac B.; Pimentel, Cecilio; Souza, Richard Demo; Uchôa-Filho, Bartolomeu F.

    2014-12-01

    This paper presents a computational complexity measure of convolutional codes well suitable for software implementations of the Viterbi algorithm (VA) operating with hard decision. We investigate the number of arithmetic operations performed by the decoding process over the conventional and minimal trellis modules. A relation between the complexity measure defined in this work and the one defined by McEliece and Lin is investigated. We also conduct a refined computer search for good convolutional codes (in terms of distance spectrum) with respect to two minimal trellis complexity measures. Finally, the computational cost of implementation of each arithmetic operation is determined in terms of machine cycles taken by its execution using a typical digital signal processor widely used for low-power telecommunications applications.

  9. Convolution using guided acoustooptical interaction in thin-film waveguides

    NASA Technical Reports Server (NTRS)

    Chang, W. S. C.; Becker, R. A.; Tsai, C. S.; Yao, I. W.

    1977-01-01

    Interaction of two antiparallel acoustic surface waves (ASW) with an optical guided wave has been investigated theoretically as well as experimentally to obtain the convolution of two ASW signals. The maximum time-bandwidth product that can be achieved by such a convolver is shown to be of the order of 1000 or more. The maximum dynamic range can be as large as 83 dB.

  10. New syndrome decoder for (n, 1) convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1983-01-01

    The letter presents a new syndrome decoding algorithm for the (n, 1) convolutional codes (CC) that is different and simpler than the previous syndrome decoding algorithm of Schalkwijk and Vinck. The new technique uses the general solution of the polynomial linear Diophantine equation for the error polynomial vector E(D). A recursive, Viterbi-like, algorithm is developed to find the minimum weight error vector E(D). An example is given for the binary nonsystematic (2, 1) CC.

  11. Face Detection Using GPU-Based Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Nasse, Fabian; Thurau, Christian; Fink, Gernot A.

    In this paper, we consider the problem of face detection under pose variations. Unlike other contributions, a focus of this work resides within efficient implementation utilizing the computational powers of modern graphics cards. The proposed system consists of a parallelized implementation of convolutional neural networks (CNNs) with a special emphasize on also parallelizing the detection process. Experimental validation in a smart conference room with 4 active ceiling-mounted cameras shows a dramatic speed-gain under real-life conditions.

  12. Fine-grained representation learning in convolutional autoencoders

    NASA Astrophysics Data System (ADS)

    Luo, Chang; Wang, Jie

    2016-03-01

    Convolutional autoencoders (CAEs) have been widely used as unsupervised feature extractors for high-resolution images. As a key component in CAEs, pooling is a biologically inspired operation to achieve scale and shift invariances, and the pooled representation directly affects the CAEs' performance. Fine-grained pooling, which uses small and dense pooling regions, encodes fine-grained visual cues and enhances local characteristics. However, it tends to be sensitive to spatial rearrangements. In most previous works, pooled features were obtained by empirically modulating parameters in CAEs. We see the CAE as a whole and propose a fine-grained representation learning law to extract better fine-grained features. This representation learning law suggests two directions for improvement. First, we probabilistically evaluate the discrimination-invariance tradeoff with fine-grained granularity in the pooled feature maps, and suggest the proper filter scale in the convolutional layer and appropriate whitening parameters in preprocessing step. Second, pooling approaches are combined with the sparsity degree in pooling regions, and we propose the preferable pooling approach. Experimental results on two independent benchmark datasets demonstrate that our representation learning law could guide CAEs to extract better fine-grained features and performs better in multiclass classification task. This paper also provides guidance for selecting appropriate parameters to obtain better fine-grained representation in other convolutional neural networks.

  13. Automatic localization of vertebrae based on convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Shen, Wei; Yang, Feng; Mu, Wei; Yang, Caiyun; Yang, Xin; Tian, Jie

    2015-03-01

    Localization of the vertebrae is of importance in many medical applications. For example, the vertebrae can serve as the landmarks in image registration. They can also provide a reference coordinate system to facilitate the localization of other organs in the chest. In this paper, we propose a new vertebrae localization method using convolutional neural networks (CNN). The main advantage of the proposed method is the removal of hand-crafted features. We construct two training sets to train two CNNs that share the same architecture. One is used to distinguish the vertebrae from other tissues in the chest, and the other is aimed at detecting the centers of the vertebrae. The architecture contains two convolutional layers, both of which are followed by a max-pooling layer. Then the output feature vector from the maxpooling layer is fed into a multilayer perceptron (MLP) classifier which has one hidden layer. Experiments were performed on ten chest CT images. We used leave-one-out strategy to train and test the proposed method. Quantitative comparison between the predict centers and ground truth shows that our convolutional neural networks can achieve promising localization accuracy without hand-crafted features.

  14. Fast space-varying convolution and its application in stray light reduction

    NASA Astrophysics Data System (ADS)

    Wei, Jianing; Cao, Guangzhi; Bouman, Charles A.; Allebach, Jan P.

    2009-02-01

    Space-varying convolution often arises in the modeling or restoration of images captured by optical imaging systems. For example, in applications such as microscopy or photography the distortions introduced by lenses typically vary across the field of view, so accurate restoration also requires the use of space-varying convolution. While space-invariant convolution can be efficiently implemented with the Fast Fourier Transform (FFT), space-varying convolution requires direct implementation of the convolution operation, which can be very computationally expensive when the convolution kernel is large. In this paper, we develop a general approach to the efficient implementation of space-varying convolution through the use of matrix source coding techniques. This method can dramatically reduce computation by approximately factoring the dense space-varying convolution operator into a product of sparse transforms. This approach leads to a tradeoff between the accuracy and speed of the operation that is closely related to the distortion-rate tradeoff that is commonly made in lossy source coding. We apply our method to the problem of stray light reduction for digital photographs, where convolution with a spatially varying stray light point spread function is required. The experimental results show that our algorithm can achieve a dramatic reduction in computation while achieving high accuracy.

  15. Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks

    NASA Astrophysics Data System (ADS)

    Zuo, Zhen; Shuai, Bing; Wang, Gang; Liu, Xiao; Wang, Xingxing; Wang, Bing; Chen, Yushi

    2016-07-01

    Existing deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local image areas without considering the dependencies among different image regions. However, such dependencies are very important for generating explicit image representation. In contrast, recurrent neural networks (RNNs) are well known for their ability of encoding contextual information among sequential data, and they only require a limited number of network parameters. General RNNs can hardly be directly applied on non-sequential data. Thus, we proposed the hierarchical RNNs (HRNNs). In HRNNs, each RNN layer focuses on modeling spatial dependencies among image regions from the same scale but different locations. While the cross RNN scale connections target on modeling scale dependencies among regions from the same location but different scales. Specifically, we propose two recurrent neural network models: 1) hierarchical simple recurrent network (HSRN), which is fast and has low computational cost; and 2) hierarchical long-short term memory recurrent network (HLSTM), which performs better than HSRN with the price of more computational cost. In this manuscript, we integrate CNNs with HRNNs, and develop end-to-end convolutional hierarchical recurrent neural networks (C-HRNNs). C-HRNNs not only make use of the representation power of CNNs, but also efficiently encodes spatial and scale dependencies among different image regions. On four of the most challenging object/scene image classification benchmarks, our C-HRNNs achieve state-of-the-art results on Places 205, SUN 397, MIT indoor, and competitive results on ILSVRC 2012.

  16. Faster GPU-based convolutional gridding via thread coarsening

    NASA Astrophysics Data System (ADS)

    Merry, B.

    2016-07-01

    Convolutional gridding is a processor-intensive step in interferometric imaging. While it is possible to use graphics processing units (GPUs) to accelerate this operation, existing methods use only a fraction of the available flops. We apply thread coarsening to improve the efficiency of an existing algorithm, and observe performance gains of up to 3.2 × for single-polarization gridding and 1.9 × for quad-polarization gridding on a GeForce GTX 980, and smaller but still significant gains on a Radeon R9 290X.

  17. Simplified Syndrome Decoding of (n, 1) Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1983-01-01

    A new syndrome decoding algorithm for the (n, 1) convolutional codes (CC) that is different and simpler than the previous syndrome decoding algorithm of Schalkwijk and Vinck is presented. The new algorithm uses the general solution of the polynomial linear Diophantine equation for the error polynomial vector E(D). This set of Diophantine solutions is a coset of the CC space. A recursive or Viterbi-like algorithm is developed to find the minimum weight error vector cirumflex E(D) in this error coset. An example illustrating the new decoding algorithm is given for the binary nonsymmetric (2,1)CC.

  18. New syndrome decoding techniques for the (n, k) convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1984-01-01

    This paper presents a new syndrome decoding algorithm for the (n, k) convolutional codes (CC) which differs completely from an earlier syndrome decoding algorithm of Schalkwijk and Vinck. The new algorithm is based on the general solution of the syndrome equation, a linear Diophantine equation for the error polynomial vector E(D). The set of Diophantine solutions is a coset of the CC. In this error coset a recursive, Viterbi-like algorithm is developed to find the minimum weight error vector (circumflex)E(D). An example, illustrating the new decoding algorithm, is given for the binary nonsystemmatic (3, 1)CC. Previously announced in STAR as N83-34964

  19. New Syndrome Decoding Techniques for the (n, K) Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1983-01-01

    This paper presents a new syndrome decoding algorithm for the (n,k) convolutional codes (CC) which differs completely from an earlier syndrome decoding algorithm of Schalkwijk and Vinck. The new algorithm is based on the general solution of the syndrome equation, a linear Diophantine equation for the error polynomial vector E(D). The set of Diophantine solutions is a coset of the CC. In this error coset a recursive, Viterbi-like algorithm is developed to find the minimum weight error vector (circumflex)E(D). An example, illustrating the new decoding algorithm, is given for the binary nonsystemmatic (3,1)CC.

  20. Convolution seal for transition duct in turbine system

    SciTech Connect

    Flanagan, James Scott; LeBegue, Jeffrey Scott; McMahan, Kevin Weston; Dillard, Daniel Jackson; Pentecost, Ronnie Ray

    2015-05-26

    A turbine system is disclosed. In one embodiment, the turbine system includes a transition duct. The transition duct includes an inlet, an outlet, and a passage extending between the inlet and the outlet and defining a longitudinal axis, a radial axis, and a tangential axis. The outlet of the transition duct is offset from the inlet along the longitudinal axis and the tangential axis. The transition duct further includes an interface feature for interfacing with an adjacent transition duct. The turbine system further includes a convolution seal contacting the interface feature to provide a seal between the interface feature and the adjacent transition duct.

  1. Convolution seal for transition duct in turbine system

    SciTech Connect

    Flanagan, James Scott; LeBegue, Jeffrey Scott; McMahan, Kevin Weston; Dillard, Daniel Jackson; Pentecost, Ronnie Ray

    2015-03-10

    A turbine system is disclosed. In one embodiment, the turbine system includes a transition duct. The transition duct includes an inlet, an outlet, and a passage extending between the inlet and the outlet and defining a longitudinal axis, a radial axis, and a tangential axis. The outlet of the transition duct is offset from the inlet along the longitudinal axis and the tangential axis. The transition duct further includes an interface member for interfacing with a turbine section. The turbine system further includes a convolution seal contacting the interface member to provide a seal between the interface member and the turbine section.

  2. Convolutional neural networks for mammography mass lesion classification.

    PubMed

    Arevalo, John; Gonzalez, Fabio A; Ramos-Pollan, Raul; Oliveira, Jose L; Guevara Lopez, Miguel Angel

    2015-08-01

    Feature extraction is a fundamental step when mammography image analysis is addressed using learning based approaches. Traditionally, problem dependent handcrafted features are used to represent the content of images. An alternative approach successfully applied in other domains is the use of neural networks to automatically discover good features. This work presents an evaluation of convolutional neural networks to learn features for mammography mass lesions before feeding them to a classification stage. Experimental results showed that this approach is a suitable strategy outperforming the state-of-the-art representation from 79.9% to 86% in terms of area under the ROC curve. PMID:26736382

  3. Convolutional neural networks for synthetic aperture radar classification

    NASA Astrophysics Data System (ADS)

    Profeta, Andrew; Rodriguez, Andres; Clouse, H. Scott

    2016-05-01

    For electro-optical object recognition, convolutional neural networks (CNNs) are the state-of-the-art. For large datasets, CNNs are able to learn meaningful features used for classification. However, their application to synthetic aperture radar (SAR) has been limited. In this work we experimented with various CNN architectures on the MSTAR SAR dataset. As the input to the CNN we used the magnitude and phase (2 channels) of the SAR imagery. We used the deep learning toolboxes CAFFE and Torch7. Our results show that we can achieve 93% accuracy on the MSTAR dataset using CNNs.

  4. A digital model for streamflow routing by convolution methods

    USGS Publications Warehouse

    Doyle, W.H., Jr.; Shearman, H.O.; Stiltner, G.J.; Krug, W.O.

    1984-01-01

    U.S. Geological Survey computer model, CONROUT, for routing streamflow by unit-response convolution flow-routing techniques from an upstream channel location to a downstream channel location has been developed and documented. Calibration and verification of the flow-routing model and subsequent use of the model for simulation is also documented. Three hypothetical examples and two field applications are presented to illustrate basic flow-routing concepts. Most of the discussion is limited to daily flow routing since, to date, all completed and current studies of this nature involve daily flow routing. However, the model is programmed to accept hourly flow-routing data. (USGS)

  5. A Fortran 90 code for magnetohydrodynamics. Part 1, Banded convolution

    SciTech Connect

    Walker, D.W.

    1992-03-01

    This report describes progress in developing a Fortran 90 version of the KITE code for studying plasma instabilities in Tokamaks. In particular, the evaluation of convolution terms appearing in the numerical solution is discussed, and timing results are presented for runs performed on an 8k processor Connection Machine (CM-2). Estimates of the performance on a full-size 64k CM-2 are given, and range between 100 and 200 Mflops. The advantages of having a Fortran 90 version of the KITE code are stressed, and the future use of such a code on the newly announced CM5 and Paragon computers, from Thinking Machines Corporation and Intel, is considered.

  6. Solvent directed fabrication of Bi{sub 2}WO{sub 6} nanostructures with different morphologies: Synthesis and their shape-dependent photocatalytic properties

    SciTech Connect

    Mi, Yuwei; Zeng, Suyuan; Li, Lei; Zhang, Qingfu; Wang, Suna; Liu, Caihua; Sun, Dezhi

    2012-09-15

    Graphical abstract: The morphologies of the Bi{sub 2}WO{sub 6} nanostructures can be easily tuned by altering the solvent composition during the reaction, which will yield flower-like, pancake-like and tubular nanostructures, respectively. Highlights: ► The morphologies of Bi{sub 2}WO{sub 6} can be controlled by tuning the solvent composition. ► The effects of solvent on the morphologies of Bi{sub 2}WO{sub 6} were carefully investigated. ► The growth mechanisms for the as-prepared samples were investigated. ► The morphologies of the samples greatly affect their photocatalytic activities. -- Abstract: In this work, Bi{sub 2}WO{sub 6} with complex morphologies, namely, flower-like, pancake-like, and tubular shapes have been controllably synthesized by a facile solvothermal process. The as-obtained samples are systematically investigated using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM) and high resolution transmission electron microscopy (HRTEM). The effects of solvents on the morphologies of Bi{sub 2}WO{sub 6} nanostructures are systematically investigated. According to the time-dependent experiments, a two-step growth mode basing on Ostwald ripening process and self-assembly has been proposed for the formation of the flower-like and pancake-like Bi{sub 2}WO{sub 6} nanostructures. The photocatalytic properties of Bi{sub 2}WO{sub 6} nanostructures are strongly dependent on their shapes, sizes, and structures for the degradation of rhodamine B (RhB) under visible-light irradiation. The deduced reasons for the differences in the photocatalytic activities of these Bi{sub 2}WO{sub 6} nanostructures are further discussed.

  7. 10-7 contrast ratio at 4.5λ/D: New results obtained in laboratory experiments using nano-fabricated coronagraph and multi-Gaussian shaped pupil masks

    NASA Astrophysics Data System (ADS)

    Chakraborty, Abhijit; Thompson, Laird A.; Rogosky, Michael

    2005-04-01

    We present here new experimental results on high contrast imaging of 10-7 at 4.λ/D (λ=0.820 microns) by combining a circular focal plane mask (coronagraph) of 2.5λ/D diameter and a multi-Gaussian pupil plane mask. Both the masks were fabricated on very high surface quality (λ/30) BK7 optical substrates using nano-fabrication techniques of photolithography and metal lift-off. This process ensured that the shaped masks have a useable edge roughness better than λ/4 (rms error better than 0.2 microns), a specification that is necessary to realize the predicted theoretical limits of any mask design. Though a theoretical model predicts a contrast level of 10-12, the background noise of the observed images was speckle dominated which reduced the contrast level to 4x10-7 at 4.5λ/D. The optical setup was built on the University of Illinois Seeing Improvement System (UnISIS) optics table which is at the Coude focus of the 2.5-m telescope of the Mt. Wilson Observatory. We used a 0.820 micron laser source coupled with a 5 micron single-mode fiber to simulate an artificial star on the optical test bench of UnISIS.

  8. Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data

    NASA Astrophysics Data System (ADS)

    Anirudh, Rushil; Thiagarajan, Jayaraman J.; Bremer, Timo; Kim, Hyojin

    2016-03-01

    Early detection of lung nodules is currently the one of the most effective ways to predict and treat lung cancer. As a result, the past decade has seen a lot of focus on computer aided diagnosis (CAD) of lung nodules, whose goal is to efficiently detect, segment lung nodules and classify them as being benign or malignant. Effective detection of such nodules remains a challenge due to their arbitrariness in shape, size and texture. In this paper, we propose to employ 3D convolutional neural networks (CNN) to learn highly discriminative features for nodule detection in lieu of hand-engineered ones such as geometric shape or texture. While 3D CNNs are promising tools to model the spatio-temporal statistics of data, they are limited by their need for detailed 3D labels, which can be prohibitively expensive when compared obtaining 2D labels. Existing CAD methods rely on obtaining detailed labels for lung nodules, to train models, which is also unrealistic and time consuming. To alleviate this challenge, we propose a solution wherein the expert needs to provide only a point label, i.e., the central pixel of of the nodule, and its largest expected size. We use unsupervised segmentation to grow out a 3D region, which is used to train the CNN. Using experiments on the SPIE-LUNGx dataset, we show that the network trained using these weak labels can produce reasonably low false positive rates with a high sensitivity, even in the absence of accurate 3D labels.

  9. Fabrication of a Kevlar liner assembly

    SciTech Connect

    Schloman, A.H.

    1980-07-01

    Several liner assemblies were fabricated with Kevlar 49 and epoxy using various wet layup and prepreg processes. A production process, using prepreg material, was developed for fabricating the liner and a wet layup molding process was used to fabricate the Kevlar hat-shaped tunnels. Fabrication of the tunnels using Kevlar prepreg with an autoclave curving process was evaluated.

  10. Fast space-varying convolution using matrix source coding with applications to camera stray light reduction.

    PubMed

    Wei, Jianing; Bouman, Charles A; Allebach, Jan P

    2014-05-01

    Many imaging applications require the implementation of space-varying convolution for accurate restoration and reconstruction of images. Here, we use the term space-varying convolution to refer to linear operators whose impulse response has slow spatial variation. In addition, these space-varying convolution operators are often dense, so direct implementation of the convolution operator is typically computationally impractical. One such example is the problem of stray light reduction in digital cameras, which requires the implementation of a dense space-varying deconvolution operator. However, other inverse problems, such as iterative tomographic reconstruction, can also depend on the implementation of dense space-varying convolution. While space-invariant convolution can be efficiently implemented with the fast Fourier transform, this approach does not work for space-varying operators. So direct convolution is often the only option for implementing space-varying convolution. In this paper, we develop a general approach to the efficient implementation of space-varying convolution, and demonstrate its use in the application of stray light reduction. Our approach, which we call matrix source coding, is based on lossy source coding of the dense space-varying convolution matrix. Importantly, by coding the transformation matrix, we not only reduce the memory required to store it; we also dramatically reduce the computation required to implement matrix-vector products. Our algorithm is able to reduce computation by approximately factoring the dense space-varying convolution operator into a product of sparse transforms. Experimental results show that our method can dramatically reduce the computation required for stray light reduction while maintaining high accuracy. PMID:24710398

  11. Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.

    PubMed

    Dürr, Oliver; Sick, Beate

    2016-10-01

    Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening-based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%.

  12. Convolutional neural network architectures for predicting DNA–protein binding

    PubMed Central

    Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

    2016-01-01

    Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

  13. Convolutional Neural Network Based Fault Detection for Rotating Machinery

    NASA Astrophysics Data System (ADS)

    Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie

    2016-09-01

    Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.

  14. Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks.

    PubMed

    Dosovitskiy, Alexey; Fischer, Philipp; Springenberg, Jost Tobias; Riedmiller, Martin; Brox, Thomas

    2016-09-01

    Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor. PMID:26540673

  15. Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks.

    PubMed

    Dosovitskiy, Alexey; Fischer, Philipp; Springenberg, Jost Tobias; Riedmiller, Martin; Brox, Thomas

    2016-09-01

    Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.

  16. A Mathematical Motivation for Complex-Valued Convolutional Networks.

    PubMed

    Tygert, Mark; Bruna, Joan; Chintala, Soumith; LeCun, Yann; Piantino, Serkan; Szlam, Arthur

    2016-05-01

    A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an input vector of nonnegative real numbers: (1) convolution with complex-valued vectors, followed by (2) taking the absolute value of every entry of the resulting vectors, followed by (3) local averaging. For processing real-valued random vectors, complex-valued convnets can be viewed as data-driven multiscale windowed power spectra, data-driven multiscale windowed absolute spectra, data-driven multiwavelet absolute values, or (in their most general configuration) data-driven nonlinear multiwavelet packets. Indeed, complex-valued convnets can calculate multiscale windowed spectra when the convnet filters are windowed complex-valued exponentials. Standard real-valued convnets, using rectified linear units (ReLUs), sigmoidal (e.g., logistic or tanh) nonlinearities, or max pooling, for example, do not obviously exhibit the same exact correspondence with data-driven wavelets (whereas for complex-valued convnets, the correspondence is much more than just a vague analogy). Courtesy of the exact correspondence, the remarkably rich and rigorous body of mathematical analysis for wavelets applies directly to (complex-valued) convnets.

  17. Enhancing Neutron Beam Production with a Convoluted Moderator

    SciTech Connect

    Iverson, Erik B; Baxter, David V; Muhrer, Guenter; Ansell, Stuart; Gallmeier, Franz X; Dalgliesh, Robert; Lu, Wei; Kaiser, Helmut

    2014-10-01

    We describe a new concept for a neutron moderating assembly resulting in the more efficient production of slow neutron beams. The Convoluted Moderator, a heterogeneous stack of interleaved moderating material and nearly transparent single-crystal spacers, is a directionally-enhanced neutron beam source, improving beam effectiveness over an angular range comparable to the range accepted by neutron beam lines and guides. We have demonstrated gains of 50% in slow neutron intensity for a given fast neutron production rate while simultaneously reducing the wavelength-dependent emission time dispersion by 25%, both coming from a geometric effect in which the neutron beam lines view a large surface area of moderating material in a relatively small volume. Additionally, we have confirmed a Bragg-enhancement effect arising from coherent scattering within the single-crystal spacers. We have not observed hypothesized refractive effects leading to additional gains at long wavelength. In addition to confirmation of the validity of the Convoluted Moderator concept, our measurements provide a series of benchmark experiments suitable for developing simulation and analysis techniques for practical optimization and eventual implementation at slow neutron source facilities.

  18. Deep Convolutional Neural Networks for large-scale speech tasks.

    PubMed

    Sainath, Tara N; Kingsbury, Brian; Saon, George; Soltau, Hagen; Mohamed, Abdel-rahman; Dahl, George; Ramabhadran, Bhuvana

    2015-04-01

    Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, we hypothesize that CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs). In this paper, we explore applying CNNs to large vocabulary continuous speech recognition (LVCSR) tasks. First, we determine the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks. Specifically, we focus on how many convolutional layers are needed, what is an appropriate number of hidden units, what is the best pooling strategy. Second, investigate how to incorporate speaker-adapted features, which cannot directly be modeled by CNNs as they do not obey locality in frequency, into the CNN framework. Third, given the importance of sequence training for speech tasks, we introduce a strategy to use ReLU+dropout during Hessian-free sequence training of CNNs. Experiments on 3 LVCSR tasks indicate that a CNN with the proposed speaker-adapted and ReLU+dropout ideas allow for a 12%-14% relative improvement in WER over a strong DNN system, achieving state-of-the art results in these 3 tasks.

  19. Shape-dependent electron transfer kinetics and catalytic activity of NiO nanoparticles immobilized onto DNA modified electrode: fabrication of highly sensitive enzymeless glucose sensor.

    PubMed

    Sharifi, Ensiyeh; Salimi, Abdollah; Shams, Esmaeil; Noorbakhsh, Abdollah; Amini, Mohammad K

    2014-06-15

    Herein we describe improved electron transfer properties and catalytic activity of nickel oxide nanoparticles (NiONPs) via the electrochemical deposition on DNA modified glassy carbon electrode (DNA/GCE) surface. NiONPs deposited on the bare and DNA-coated GCE showed different morphologies, electrochemical kinetics and catalytic activities. The atomic force microscopy (AFM) images revealed the formation of triangular NPs on the DNA/GCE that followed the shape produced by the DNA template, while the electrodeposition of NiONPs on the bare GCE surface led to the formation of spherical nanoparticles. Electrochemical impedance spectroscopy (EIS) measurements revealed lower charge-transfer resistance (Rct) of triangular NiONPs compared to spherical NPs. Furthermore, the electrocatalytic activity of triangular NiONPs compared to spherical NPs toward glucose oxidation in alkaline media was significantly improved. The amperometric oxidation of glucose at NiONP-DNA/GCE, yielded a very high sensitivity of 17.32 mA mM(-1)cm(-2) and an unprecedented detection limit of 17 nM. The enhanced electron transfer properties and electrocatalytic activity of NiONP-DNA/GCE can be attributed to the higher fraction of sharp corners and edges present in the triangular NiONPs compared to the spherical NPs. The developed sensor was successfully applied to the determination of glucose in serum samples. PMID:24525015

  20. Shape-dependent electron transfer kinetics and catalytic activity of NiO nanoparticles immobilized onto DNA modified electrode: fabrication of highly sensitive enzymeless glucose sensor.

    PubMed

    Sharifi, Ensiyeh; Salimi, Abdollah; Shams, Esmaeil; Noorbakhsh, Abdollah; Amini, Mohammad K

    2014-06-15

    Herein we describe improved electron transfer properties and catalytic activity of nickel oxide nanoparticles (NiONPs) via the electrochemical deposition on DNA modified glassy carbon electrode (DNA/GCE) surface. NiONPs deposited on the bare and DNA-coated GCE showed different morphologies, electrochemical kinetics and catalytic activities. The atomic force microscopy (AFM) images revealed the formation of triangular NPs on the DNA/GCE that followed the shape produced by the DNA template, while the electrodeposition of NiONPs on the bare GCE surface led to the formation of spherical nanoparticles. Electrochemical impedance spectroscopy (EIS) measurements revealed lower charge-transfer resistance (Rct) of triangular NiONPs compared to spherical NPs. Furthermore, the electrocatalytic activity of triangular NiONPs compared to spherical NPs toward glucose oxidation in alkaline media was significantly improved. The amperometric oxidation of glucose at NiONP-DNA/GCE, yielded a very high sensitivity of 17.32 mA mM(-1)cm(-2) and an unprecedented detection limit of 17 nM. The enhanced electron transfer properties and electrocatalytic activity of NiONP-DNA/GCE can be attributed to the higher fraction of sharp corners and edges present in the triangular NiONPs compared to the spherical NPs. The developed sensor was successfully applied to the determination of glucose in serum samples.

  1. Design and fabrication of a bending rotation fatigue test rig for in situ electrochemical analysis during fatigue testing of NiTi shape memory alloy wires.

    PubMed

    Neelakantan, Lakshman; Zglinski, Jenni Kristin; Frotscher, Matthias; Eggeler, Gunther

    2013-03-01

    The current investigation proposes a novel method for simultaneous assessment of the electrochemical and structural fatigue properties of nickel-titanium shape memory alloy (NiTi SMA) wires. The design and layout of an in situ electrochemical cell in a custom-made bending rotation fatigue (BRF) test rig is presented. This newly designed test rig allows performing a wide spectrum of experiments for studying the influence of fatigue on corrosion and vice versa. This can be achieved by performing ex situ and∕or in situ measurements. The versatility of the combined electrochemical∕mechanical test rig is demonstrated by studying the electrochemical behavior of NiTi SMA wires in 0.9% NaCl electrolyte under load. The ex situ measurements allow addressing various issues, for example, the influence of pre-fatigue on the localized corrosion resistance, or the influence of hydrogen on fatigue life. Ex situ experiments showed that a pre-fatigued wire is more susceptible to localized corrosion. The synergetic effect can be concluded from the polarization studies and specifically from an in situ study of the open circuit potential (OCP) transients, which sensitively react to the elementary repassivation events related to the local failure of the oxide layer. It can also be used as an indicator for identifying the onset of the fatigue failure. PMID:23556847

  2. Design and fabrication of a bending rotation fatigue test rig for in situ electrochemical analysis during fatigue testing of NiTi shape memory alloy wires

    NASA Astrophysics Data System (ADS)

    Neelakantan, Lakshman; Zglinski, Jenni Kristin; Frotscher, Matthias; Eggeler, Gunther

    2013-03-01

    The current investigation proposes a novel method for simultaneous assessment of the electrochemical and structural fatigue properties of nickel-titanium shape memory alloy (NiTi SMA) wires. The design and layout of an in situ electrochemical cell in a custom-made bending rotation fatigue (BRF) test rig is presented. This newly designed test rig allows performing a wide spectrum of experiments for studying the influence of fatigue on corrosion and vice versa. This can be achieved by performing ex situ and/or in situ measurements. The versatility of the combined electrochemical/mechanical test rig is demonstrated by studying the electrochemical behavior of NiTi SMA wires in 0.9% NaCl electrolyte under load. The ex situ measurements allow addressing various issues, for example, the influence of pre-fatigue on the localized corrosion resistance, or the influence of hydrogen on fatigue life. Ex situ experiments showed that a pre-fatigued wire is more susceptible to localized corrosion. The synergetic effect can be concluded from the polarization studies and specifically from an in situ study of the open circuit potential (OCP) transients, which sensitively react to the elementary repassivation events related to the local failure of the oxide layer. It can also be used as an indicator for identifying the onset of the fatigue failure.

  3. Design and fabrication of a bending rotation fatigue test rig for in situ electrochemical analysis during fatigue testing of NiTi shape memory alloy wires

    SciTech Connect

    Neelakantan, Lakshman; Zglinski, Jenni Kristin; Eggeler, Gunther; Frotscher, Matthias

    2013-03-15

    The current investigation proposes a novel method for simultaneous assessment of the electrochemical and structural fatigue properties of nickel-titanium shape memory alloy (NiTi SMA) wires. The design and layout of an in situ electrochemical cell in a custom-made bending rotation fatigue (BRF) test rig is presented. This newly designed test rig allows performing a wide spectrum of experiments for studying the influence of fatigue on corrosion and vice versa. This can be achieved by performing ex situ and/or in situ measurements. The versatility of the combined electrochemical/mechanical test rig is demonstrated by studying the electrochemical behavior of NiTi SMA wires in 0.9% NaCl electrolyte under load. The ex situ measurements allow addressing various issues, for example, the influence of pre-fatigue on the localized corrosion resistance, or the influence of hydrogen on fatigue life. Ex situ experiments showed that a pre-fatigued wire is more susceptible to localized corrosion. The synergetic effect can be concluded from the polarization studies and specifically from an in situ study of the open circuit potential (OCP) transients, which sensitively react to the elementary repassivation events related to the local failure of the oxide layer. It can also be used as an indicator for identifying the onset of the fatigue failure.

  4. Immobilization of electroporated cells for fabrication of cellular biosensors: physiological effects of the shape of calcium alginate matrices and foetal calf serum.

    PubMed

    Katsanakis, Nikos; Katsivelis, Andreas; Kintzios, Spiridon

    2009-01-01

    In order to investigate the physiological effect of transfected cell immobilization in calcium alginate gels, we immobilized electroporated Vero cells in gels shaped either as spherical beads or as thin membrane layers. In addition, we investigated whether serum addition had a positive effect on cell proliferation and viability in either gel configuration. The gels were stored for four weeks in a medium supplemented or not with 20% (v/v) foetal calf serum. Throughout a culture period of four weeks, cell proliferation and cell viability were assayed by optical microscopy after provision of Trypan Blue. Non-elaborate culture conditions (room temperature, non-CO(2) enriched culture atmosphere) were applied throughout the experimental period in order to evaluate cell viability under less than optimal storage conditions. Immobilization of electroporated cells was associated with an initially reduced cell viability, which was gradually increased. Immobilization was associated with maintenance of cell growth for the duration of the experimental period, whereas electroporated cells essentially died after a week in suspension culture. Considerable proliferation of immobilized cells was observed in spherical alginate beads. In both gel configurations, addition of serum was associated with increased cell proliferation. The results of the present study could contribute to an improvement of the storability of biosensors based on electroporated, genetically or membrane-engineered cells.

  5. There is no MacWilliams identity for convolutional codes. [transmission gain comparison

    NASA Technical Reports Server (NTRS)

    Shearer, J. B.; Mceliece, R. J.

    1977-01-01

    An example is provided of two convolutional codes that have the same transmission gain but whose dual codes do not. This shows that no analog of the MacWilliams identity for block codes can exist relating the transmission gains of a convolutional code and its dual.

  6. Using convolutional decoding to improve time delay and phase estimation in digital communications

    DOEpatents

    Ormesher, Richard C.; Mason, John J.

    2010-01-26

    The time delay and/or phase of a communication signal received by a digital communication receiver can be estimated based on a convolutional decoding operation that the communication receiver performs on the received communication signal. If the original transmitted communication signal has been spread according to a spreading operation, a corresponding despreading operation can be integrated into the convolutional decoding operation.

  7. Biophysical Measurements of Bacterial Cell Shape.

    PubMed

    Nguyen, Jeffrey P; Bratton, Benjamin P; Shaevitz, Joshua W

    2016-01-01

    A bacteria's shape plays a large role in determining its mechanism of motility, energy requirements, and ability to avoid predation. Although it is a major factor in cell fitness, little is known about how cell shape is determined or maintained. These problems are made worse by a lack of accurate methods to measure cell shape in vivo, as current methods do not account for blurring artifacts introduced by the microscope. Here, we introduce a method using 2D active surfaces and forward convolution with a measured point spread function to measure the 3D shape of different strains of E. coli from fluorescent images. Using this technique, we are also able to measure the distribution of fluorescent molecules, such as polymers, on the cell surface. This quantification of the surface geometry and fluorescence distribution allow for a more precise measure of 3D cell shape and is a useful tool for measuring protein localization and the mechanisms of bacterial shape control. PMID:27311676

  8. The effect of whitening transformation on pooling operations in convolutional autoencoders

    NASA Astrophysics Data System (ADS)

    Li, Zuhe; Fan, Yangyu; Liu, Weihua

    2015-12-01

    Convolutional autoencoders (CAEs) are unsupervised feature extractors for high-resolution images. In the pre-processing step, whitening transformation has widely been adopted to remove redundancy by making adjacent pixels less correlated. Pooling is a biologically inspired operation to reduce the resolution of feature maps and achieve spatial invariance in convolutional neural networks. Conventionally, pooling methods are mainly determined empirically in most previous work. Therefore, our main purpose is to study the relationship between whitening processing and pooling operations in convolutional autoencoders for image classification. We propose an adaptive pooling approach based on the concepts of information entropy to test the effect of whitening on pooling in different conditions. Experimental results on benchmark datasets indicate that the performance of pooling strategies is associated with the distribution of feature activations, which can be affected by whitening processing. This provides guidance for the selection of pooling methods in convolutional autoencoders and other convolutional neural networks.

  9. Tomography by iterative convolution - Empirical study and application to interferometry

    NASA Technical Reports Server (NTRS)

    Vest, C. M.; Prikryl, I.

    1984-01-01

    An algorithm for computer tomography has been developed that is applicable to reconstruction from data having incomplete projections because an opaque object blocks some of the probing radiation as it passes through the object field. The algorithm is based on iteration between the object domain and the projection (Radon transform) domain. Reconstructions are computed during each iteration by the well-known convolution method. Although it is demonstrated that this algorithm does not converge, an empirically justified criterion for terminating the iteration when the most accurate estimate has been computed is presented. The algorithm has been studied by using it to reconstruct several different object fields with several different opaque regions. It also has been used to reconstruct aerodynamic density fields from interferometric data recorded in wind tunnel tests.

  10. Small convolution kernels for high-fidelity image restoration

    NASA Technical Reports Server (NTRS)

    Reichenbach, Stephen E.; Park, Stephen K.

    1991-01-01

    An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

  11. Convolution properties for certain classes of multivalent functions

    NASA Astrophysics Data System (ADS)

    Sokól, Janusz; Trojnar-Spelina, Lucyna

    2008-01-01

    Recently N.E. Cho, O.S. Kwon and H.M. Srivastava [Nak Eun Cho, Oh Sang Kwon, H.M. Srivastava, Inclusion relationships and argument properties for certain subclasses of multivalent functions associated with a family of linear operators, J. Math. Anal. Appl. 292 (2004) 470-483] have introduced the class of multivalent analytic functions and have given a number of results. This class has been defined by means of a special linear operator associated with the Gaussian hypergeometric function. In this paper we have extended some of the previous results and have given other properties of this class. We have made use of differential subordinations and properties of convolution in geometric function theory.

  12. Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

    NASA Astrophysics Data System (ADS)

    Zhang, Kaipeng; Zhang, Zhanpeng; Li, Zhifeng; Qiao, Yu

    2016-10-01

    Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this paper, we propose a deep cascaded multi-task framework which exploits the inherent correlation between them to boost up their performance. In particular, our framework adopts a cascaded structure with three stages of carefully designed deep convolutional networks that predict face and landmark location in a coarse-to-fine manner. In addition, in the learning process, we propose a new online hard sample mining strategy that can improve the performance automatically without manual sample selection. Our method achieves superior accuracy over the state-of-the-art techniques on the challenging FDDB and WIDER FACE benchmark for face detection, and AFLW benchmark for face alignment, while keeps real time performance.

  13. Deep convolutional neural networks for ATR from SAR imagery

    NASA Astrophysics Data System (ADS)

    Morgan, David A. E.

    2015-05-01

    Deep architectures for classification and representation learning have recently attracted significant attention within academia and industry, with many impressive results across a diverse collection of problem sets. In this work we consider the specific application of Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) data from the MSTAR public release data set. The classification performance achieved using a Deep Convolutional Neural Network (CNN) on this data set was found to be competitive with existing methods considered to be state-of-the-art. Unlike most existing algorithms, this approach can learn discriminative feature sets directly from training data instead of requiring pre-specification or pre-selection by a human designer. We show how this property can be exploited to efficiently adapt an existing classifier to recognise a previously unseen target and discuss potential practical applications.

  14. Drug-Drug Interaction Extraction via Convolutional Neural Networks.

    PubMed

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong

    2016-01-01

    Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%. PMID:26941831

  15. Drug-Drug Interaction Extraction via Convolutional Neural Networks.

    PubMed

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong

    2016-01-01

    Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.

  16. Enhanced Line Integral Convolution with Flow Feature Detection

    NASA Technical Reports Server (NTRS)

    Lane, David; Okada, Arthur

    1996-01-01

    The Line Integral Convolution (LIC) method, which blurs white noise textures along a vector field, is an effective way to visualize overall flow patterns in a 2D domain. The method produces a flow texture image based on the input velocity field defined in the domain. Because of the nature of the algorithm, the texture image tends to be blurry. This sometimes makes it difficult to identify boundaries where flow separation and reattachments occur. We present techniques to enhance LIC texture images and use colored texture images to highlight flow separation and reattachment boundaries. Our techniques have been applied to several flow fields defined in 3D curvilinear multi-block grids and scientists have found the results to be very useful.

  17. Plane-wave decomposition by spherical-convolution microphone array

    NASA Astrophysics Data System (ADS)

    Rafaely, Boaz; Park, Munhum

    2001-05-01

    Reverberant sound fields are widely studied, as they have a significant influence on the acoustic performance of enclosures in a variety of applications. For example, the intelligibility of speech in lecture rooms, the quality of music in auditoria, the noise level in offices, and the production of 3D sound in living rooms are all affected by the enclosed sound field. These sound fields are typically studied through frequency response measurements or statistical measures such as reverberation time, which do not provide detailed spatial information. The aim of the work presented in this seminar is the detailed analysis of reverberant sound fields. A measurement and analysis system based on acoustic theory and signal processing, designed around a spherical microphone array, is presented. Detailed analysis is achieved by decomposition of the sound field into waves, using spherical Fourier transform and spherical convolution. The presentation will include theoretical review, simulation studies, and initial experimental results.

  18. Fast convolution with free-space Green's functions

    NASA Astrophysics Data System (ADS)

    Vico, Felipe; Greengard, Leslie; Ferrando, Miguel

    2016-10-01

    We introduce a fast algorithm for computing volume potentials - that is, the convolution of a translation invariant, free-space Green's function with a compactly supported source distribution defined on a uniform grid. The algorithm relies on regularizing the Fourier transform of the Green's function by cutting off the interaction in physical space beyond the domain of interest. This permits the straightforward application of trapezoidal quadrature and the standard FFT, with superalgebraic convergence for smooth data. Moreover, the method can be interpreted as employing a Nystrom discretization of the corresponding integral operator, with matrix entries which can be obtained explicitly and rapidly. This is of use in the design of preconditioners or fast direct solvers for a variety of volume integral equations. The method proposed permits the computation of any derivative of the potential, at the cost of an additional FFT.

  19. Invariant Descriptor Learning Using a Siamese Convolutional Neural Network

    NASA Astrophysics Data System (ADS)

    Chen, L.; Rottensteiner, F.; Heipke, C.

    2016-06-01

    In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.

  20. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.

    PubMed

    He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian

    2015-09-01

    Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 × faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.

  1. Mitochondrial and metabolic dysfunction in renal convoluted tubules of obese mice: protective role of melatonin.

    PubMed

    Stacchiotti, Alessandra; Favero, Gaia; Giugno, Lorena; Lavazza, Antonio; Reiter, Russel J; Rodella, Luigi Fabrizio; Rezzani, Rita

    2014-01-01

    Obesity is a common and complex health problem, which impacts crucial organs; it is also considered an independent risk factor for chronic kidney disease. Few studies have analyzed the consequence of obesity in the renal proximal convoluted tubules, which are the major tubules involved in reabsorptive processes. For optimal performance of the kidney, energy is primarily provided by mitochondria. Melatonin, an indoleamine and antioxidant, has been identified in mitochondria, and there is considerable evidence regarding its essential role in the prevention of oxidative mitochondrial damage. In this study we evaluated the mechanism(s) of mitochondrial alterations in an animal model of obesity (ob/ob mice) and describe the beneficial effects of melatonin treatment on mitochondrial morphology and dynamics as influenced by mitofusin-2 and the intrinsic apoptotic cascade. Melatonin dissolved in 1% ethanol was added to the drinking water from postnatal week 5-13; the calculated dose of melatonin intake was 100 mg/kg body weight/day. Compared to control mice, obesity-related morphological alterations were apparent in the proximal tubules which contained round mitochondria with irregular, short cristae and cells with elevated apoptotic index. Melatonin supplementation in obese mice changed mitochondria shape and cristae organization of proximal tubules, enhanced mitofusin-2 expression, which in turn modulated the progression of the mitochondria-driven intrinsic apoptotic pathway. These changes possibly aid in reducing renal failure. The melatonin-mediated changes indicate its potential protective use against renal morphological damage and dysfunction associated with obesity and metabolic disease.

  2. Cygrid: A fast Cython-powered convolution-based gridding module for Python

    NASA Astrophysics Data System (ADS)

    Winkel, B.; Lenz, D.; Flöer, L.

    2016-06-01

    Context. Data gridding is a common task in astronomy and many other science disciplines. It refers to the resampling of irregularly sampled data to a regular grid. Aims: We present cygrid, a library module for the general purpose programming language Python. Cygrid can be used to resample data to any collection of target coordinates, although its typical application involves FITS maps or data cubes. The FITS world coordinate system standard is supported. Methods: The regridding algorithm is based on the convolution of the original samples with a kernel of arbitrary shape. We introduce a lookup table scheme that allows us to parallelize the gridding and combine it with the HEALPix tessellation of the sphere for fast neighbor searches. Results: We show that for n input data points, cygrids runtime scales between O(n) and O(nlog n) and analyze the performance gain that is achieved using multiple CPU cores. We also compare the gridding speed with other techniques, such as nearest-neighbor, and linear and cubic spline interpolation. Conclusions: Cygrid is a very fast and versatile gridding library that significantly outperforms other third-party Python modules, such as the linear and cubic spline interpolation provided by SciPy. http://https://github.com/bwinkel/cygrid

  3. Plasma evolution and dynamics in high-power vacuum-transmission-line post-hole convolutes

    NASA Astrophysics Data System (ADS)

    Rose, D. V.; Welch, D. R.; Hughes, T. P.; Clark, R. E.; Stygar, W. A.

    2008-06-01

    Vacuum-post-hole convolutes are used in pulsed high-power generators to join several magnetically insulated transmission lines (MITL) in parallel. Such convolutes add the output currents of the MITLs, and deliver the combined current to a single MITL that, in turn, delivers the current to a load. Magnetic insulation of electron flow, established upstream of the convolute region, is lost at the convolute due to symmetry breaking and the formation of magnetic nulls, resulting in some current losses. At very high-power operating levels and long pulse durations, the expansion of electrode plasmas into the MITL of such devices is considered likely. This work examines the evolution and dynamics of cathode plasmas in the double-post-hole convolutes used on the Z accelerator [R. B. Spielman , Phys. Plasmas 5, 2105 (1998)PHPAEN1070-664X10.1063/1.872881]. Three-dimensional particle-in-cell (PIC) simulations that model the entire radial extent of the Z accelerator convolute—from the parallel-plate transmission-line power feeds to the z-pinch load region—are used to determine electron losses in the convolute. The results of the simulations demonstrate that significant current losses (1.5 MA out of a total system current of 18.5 MA), which are comparable to the losses observed experimentally, could be caused by the expansion of cathode plasmas in the convolute regions.

  4. Bayesian Vision for Shape Recovery

    NASA Technical Reports Server (NTRS)

    Jalobeanu, Andre

    2004-01-01

    We present a new Bayesian vision technique that aims at recovering a shape from two or more noisy observations taken under similar lighting conditions. The shape is parametrized by a piecewise linear height field, textured by a piecewise linear irradiance field, and we assume Gaussian Markovian priors for both shape vertices and irradiance variables. The observation process. also known as rendering, is modeled by a non-affine projection (e.g. perspective projection) followed by a convolution with a piecewise linear point spread function. and contamination by additive Gaussian noise. We assume that the observation parameters are calibrated beforehand. The major novelty of the proposed method consists of marginalizing out the irradiances considered as nuisance parameters, which is achieved by Laplace approximations. This reduces the inference to minimizing an energy that only depends on the shape vertices, and therefore allows an efficient Iterated Conditional Mode (ICM) optimization scheme to be implemented. A Gaussian approximation of the posterior shape density is computed, thus providing estimates both the geometry and its uncertainty. We illustrate the effectiveness of the new method by shape reconstruction results in a 2D case. A 3D version is currently under development and aims at recovering a surface from multiple images, reconstructing the topography by marginalizing out both albedo and shading.

  5. Mechanics of a Knitted Fabric

    NASA Astrophysics Data System (ADS)

    Poincloux, Samuel; Lechenault, Frederic; Adda-Bedia, Mokhtar

    A simple knitted fabric can be seen as a topologically constrained slender rod following a periodic path. The non-linear properties of the fabric, such as large reversible deformation and characteristic shape under stress, arise from topological features known as stitches and are distinct from the constitutive yarn properties. Through experiments we studied a model stockinette fabric made of a single elastic thread, where the mechanical properties and local stitch displacements were measured. Then, we derived a model based on the yarn bending energy at the stitch level resulting in an evaluation of the displacement fields of the repetitive units which describe the fabric shape. The comparison between the predicted and the measured shape gives very good agreement and the right order of magnitude for the mechanical response is captured. This work aims at providing a fundamental framework for the understanding of knitted systems, paving the way to thread based smart materials. Contract ANR-14-CE07-0031-01 METAMAT.

  6. In Situ Fabrication Technologies

    NASA Technical Reports Server (NTRS)

    Rolin, Terry D.; Hammond, Monica

    2005-01-01

    A manufacturing system is described that is internal to controlled cabin environments which will produce functional parts to net shape with sufficient tolerance, strength and integrity to meet application specific needs such as CEV ECLS components, robotic arm or rover components, EVA suit items, unforeseen tools, conformal repair patches, and habitat fittings among others. Except for start-up and shut-down, fabrication will be automatic without crew intervention under nominal scenarios. Off-nominal scenarios may require crew and/or Earth control intervention. System will have the ability to fabricate using both provisioned feedstock materials and feedstock refined from in situ regolith.

  7. Convoluted nozzle design for the RL10 derivative 2B engine

    NASA Technical Reports Server (NTRS)

    1985-01-01

    The convoluted nozzle is a conventional refractory metal nozzle extension that is formed with a portion of the nozzle convoluted to show the extendible nozzle within the length of the rocket engine. The convoluted nozzle (CN) was deployed by a system of four gas driven actuators. For spacecraft applications the optimum CN may be self-deployed by internal pressure retained, during deployment, by a jettisonable exit closure. The convoluted nozzle is included in a study of extendible nozzles for the RL10 Engine Derivative 2B for use in an early orbit transfer vehicle (OTV). Four extendible nozzle configurations for the RL10-2B engine were evaluated. Three configurations of the two position nozzle were studied including a hydrogen dump cooled metal nozzle and radiation cooled nozzles of refractory metal and carbon/carbon composite construction respectively.

  8. Directional Radiometry and Radiative Transfer: the Convoluted Path From Centuries-old Phenomenology to Physical Optics

    NASA Technical Reports Server (NTRS)

    Mishchenko, Michael I.

    2014-01-01

    This Essay traces the centuries-long history of the phenomenological disciplines of directional radiometry and radiative transfer in turbid media, discusses their fundamental weaknesses, and outlines the convoluted process of their conversion into legitimate branches of physical optics.

  9. Toward an optimal convolutional neural network for traffic sign recognition

    NASA Astrophysics Data System (ADS)

    Habibi Aghdam, Hamed; Jahani Heravi, Elnaz; Puig, Domenec

    2015-12-01

    Convolutional Neural Networks (CNN) beat the human performance on German Traffic Sign Benchmark competition. Both the winner and the runner-up teams trained CNNs to recognize 43 traffic signs. However, both networks are not computationally efficient since they have many free parameters and they use highly computational activation functions. In this paper, we propose a new architecture that reduces the number of the parameters 27% and 22% compared with the two networks. Furthermore, our network uses Leaky Rectified Linear Units (ReLU) as the activation function that only needs a few operations to produce the result. Specifically, compared with the hyperbolic tangent and rectified sigmoid activation functions utilized in the two networks, Leaky ReLU needs only one multiplication operation which makes it computationally much more efficient than the two other functions. Our experiments on the Gertman Traffic Sign Benchmark dataset shows 0:6% improvement on the best reported classification accuracy while it reduces the overall number of parameters 85% compared with the winner network in the competition.

  10. Innervation of the renal proximal convoluted tubule of the rat

    SciTech Connect

    Barajas, L.; Powers, K. )

    1989-12-01

    Experimental data suggest the proximal tubule as a major site of neurogenic influence on tubular function. The functional and anatomical axial heterogeneity of the proximal tubule prompted this study of the distribution of innervation sites along the early, mid, and late proximal convoluted tubule (PCT) of the rat. Serial section autoradiograms, with tritiated norepinephrine serving as a marker for monoaminergic nerves, were used in this study. Freehand clay models and graphic reconstructions of proximal tubules permitted a rough estimation of the location of the innervation sites along the PCT. In the subcapsular nephrons, the early PCT (first third) was devoid of innervation sites with most of the innervation occurring in the mid (middle third) and in the late (last third) PCT. Innervation sites were found in the early PCT in nephrons located deeper in the cortex. In juxtamedullary nephrons, innervation sites could be observed on the PCT as it left the glomerulus. This gradient of PCT innervation can be explained by the different tubulovascular relationships of nephrons at different levels of the cortex. The absence of innervation sites in the early PCT of subcapsular nephrons suggests that any influence of the renal nerves on the early PCT might be due to an effect of neurotransmitter released from renal nerves reaching the early PCT via the interstitium and/or capillaries.

  11. Convolution neural-network-based detection of lung structures

    NASA Astrophysics Data System (ADS)

    Hasegawa, Akira; Lo, Shih-Chung B.; Freedman, Matthew T.; Mun, Seong K.

    1994-05-01

    Chest radiography is one of the most primary and widely used techniques in diagnostic imaging. Nowadays with the advent of digital radiology, the digital medical image processing techniques for digital chest radiographs have attracted considerable attention, and several studies on the computer-aided diagnosis (CADx) as well as on the conventional image processing techniques for chest radiographs have been reported. In the automatic diagnostic process for chest radiographs, it is important to outline the areas of the lungs, the heart, and the diaphragm. This is because the original chest radiograph is composed of important anatomic structures and, without knowing exact positions of the organs, the automatic diagnosis may result in unexpected detections. The automatic extraction of an anatomical structure from digital chest radiographs can be a useful tool for (1) the evaluation of heart size, (2) automatic detection of interstitial lung diseases, (3) automatic detection of lung nodules, and (4) data compression, etc. Based on the clearly defined boundaries of heart area, rib spaces, rib positions, and rib cage extracted, one should be able to use this information to facilitate the tasks of the CADx on chest radiographs. In this paper, we present an automatic scheme for the detection of lung field from chest radiographs by using a shift-invariant convolution neural network. A novel algorithm for smoothing boundaries of lungs is also presented.

  12. Synthesising Primary Reflections by Marchenko Redatuming and Convolutional Interferometry

    NASA Astrophysics Data System (ADS)

    Curtis, A.

    2015-12-01

    Standard active-source seismic processing and imaging steps such as velocity analysis and reverse time migration usually provide best results when all reflected waves in the input data are primaries (waves that reflect only once). Multiples (recorded waves that reflect multiple times) represent a source of coherent noise in data that must be suppressed to avoid imaging artefacts. Consequently, multiple-removal methods have been a primcipal direction of active-source seismic research for decades. We describe a new method to estimate primaries directly, which obviates the need for multiple removal. Primaries are constructed within convolutional interferometry by combining first arriving events of up-going and direct wave down-going Green's functions to virtual receivers in the subsurface. The required up-going wavefields to virtual receivers along discrete subsurface boundaries can be constructed using Marchenko redatuming. Crucially, this is possible without detailed models of the Earth's subsurface velocity structure: similarly to most migration techniques, the method only requires surface reflection data and estimates of direct (non-reflected) arrivals between subsurface sources and the acquisition surface. The method is demonstrated on a stratified synclinal model. It is shown both to improve reverse time migration compared to standard methods, and to be particularly robust against errors in the reference velocity model used.

  13. Cell osmotic water permeability of isolated rabbit proximal convoluted tubules.

    PubMed

    Carpi-Medina, P; González, E; Whittembury, G

    1983-05-01

    Cell osmotic water permeability, Pcos, of the peritubular aspect of the proximal convoluted tubule (PCT) was measured from the time course of cell volume changes subsequent to the sudden imposition of an osmotic gradient, delta Cio, across the cell membrane of PCT that had been dissected and mounted in a chamber. The possibilities of artifact were minimized. The bath was vigorously stirred, the solutions could be 95% changed within 0.1 s, and small osmotic gradients (10-20 mosM) were used. Thus, the osmotically induced water flow was a linear function of delta Cio and the effect of the 70-microns-thick unstirred layers was negligible. In addition, data were extrapolated to delta Cio = 0. Pcos for PCT was 41.6 (+/- 3.5) X 10(-4) cm3 X s-1 X osM-1 per cm2 of peritubular basal area. The standing gradient osmotic theory for transcellular osmosis is incompatible with this value. Published values for Pcos of PST are 25.1 X 10(-4), and for the transepithelial permeability Peos values are 64 X 10(-4) for PCT and 94 X 10(-4) for PST, in the same units. These results indicate that there is room for paracellular water flow in both nephron segments and that the magnitude of the transcellular and paracellular water flows may vary from one segment of the proximal tubule to another. PMID:6846543

  14. A deep convolutional neural network for recognizing foods

    NASA Astrophysics Data System (ADS)

    Jahani Heravi, Elnaz; Habibi Aghdam, Hamed; Puig, Domenec

    2015-12-01

    Controlling the food intake is an efficient way that each person can undertake to tackle the obesity problem in countries worldwide. This is achievable by developing a smartphone application that is able to recognize foods and compute their calories. State-of-art methods are chiefly based on hand-crafted feature extraction methods such as HOG and Gabor. Recent advances in large-scale object recognition datasets such as ImageNet have revealed that deep Convolutional Neural Networks (CNN) possess more representation power than the hand-crafted features. The main challenge with CNNs is to find the appropriate architecture for each problem. In this paper, we propose a deep CNN which consists of 769; 988 parameters. Our experiments show that the proposed CNN outperforms the state-of-art methods and improves the best result of traditional methods 17%. Moreover, using an ensemble of two CNNs that have been trained two different times, we are able to improve the classification performance 21:5%.

  15. Convolutional networks for fast, energy-efficient neuromorphic computing

    PubMed Central

    Esser, Steven K.; Merolla, Paul A.; Arthur, John V.; Cassidy, Andrew S.; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J.; McKinstry, Jeffrey L.; Melano, Timothy; Barch, Davis R.; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D.; Modha, Dharmendra S.

    2016-01-01

    Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer. PMID:27651489

  16. Fast convolution-superposition dose calculation on graphics hardware.

    PubMed

    Hissoiny, Sami; Ozell, Benoît; Després, Philippe

    2009-06-01

    The numerical calculation of dose is central to treatment planning in radiation therapy and is at the core of optimization strategies for modern delivery techniques. In a clinical environment, dose calculation algorithms are required to be accurate and fast. The accuracy is typically achieved through the integration of patient-specific data and extensive beam modeling, which generally results in slower algorithms. In order to alleviate execution speed problems, the authors have implemented a modern dose calculation algorithm on a massively parallel hardware architecture. More specifically, they have implemented a convolution-superposition photon beam dose calculation algorithm on a commodity graphics processing unit (GPU). They have investigated a simple porting scenario as well as slightly more complex GPU optimization strategies. They have achieved speed improvement factors ranging from 10 to 20 times with GPU implementations compared to central processing unit (CPU) implementations, with higher values corresponding to larger kernel and calculation grid sizes. In all cases, they preserved the numerical accuracy of the GPU calculations with respect to the CPU calculations. These results show that streaming architectures such as GPUs can significantly accelerate dose calculation algorithms and let envision benefits for numerically intensive processes such as optimizing strategies, in particular, for complex delivery techniques such as IMRT and are therapy.

  17. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

    PubMed

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-01

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility. PMID:26752681

  18. Accelerating Very Deep Convolutional Networks for Classification and Detection.

    PubMed

    Zhang, Xiangyu; Zou, Jianhua; He, Kaiming; Sun, Jian

    2016-10-01

    This paper aims to accelerate the test-time computation of convolutional neural networks (CNNs), especially very deep CNNs [1] that have substantially impacted the computer vision community. Unlike previous methods that are designed for approximating linear filters or linear responses, our method takes the nonlinear units into account. We develop an effective solution to the resulting nonlinear optimization problem without the need of stochastic gradient descent (SGD). More importantly, while previous methods mainly focus on optimizing one or two layers, our nonlinear method enables an asymmetric reconstruction that reduces the rapidly accumulated error when multiple (e.g., ≥ 10) layers are approximated. For the widely used very deep VGG-16 model [1] , our method achieves a whole-model speedup of 4 × with merely a 0.3 percent increase of top-5 error in ImageNet classification. Our 4 × accelerated VGG-16 model also shows a graceful accuracy degradation for object detection when plugged into the Fast R-CNN detector [2] . PMID:26599615

  19. Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks.

    PubMed

    Schlegl, Thomas; Waldstein, Sebastian M; Vogl, Wolf-Dieter; Schmidt-Erfurth, Ursula; Langs, Georg

    2015-01-01

    Learning representative computational models from medical imaging data requires large training data sets. Often, voxel-level annotation is unfeasible for sufficient amounts of data. An alternative to manual annotation, is to use the enormous amount of knowledge encoded in imaging data and corresponding reports generated during clinical routine. Weakly supervised learning approaches can link volume-level labels to image content but suffer from the typical label distributions in medical imaging data where only a small part consists of clinically relevant abnormal structures. In this paper we propose to use a semantic representation of clinical reports as a learning target that is predicted from imaging data by a convolutional neural network. We demonstrate how we can learn accurate voxel-level classifiers based on weak volume-level semantic descriptions on a set of 157 optical coherence tomography (OCT) volumes. We specifically show how semantic information increases classification accuracy for intraretinal cystoid fluid (IRC), subretinal fluid (SRF) and normal retinal tissue, and how the learning algorithm links semantic concepts to image content and geometry.

  20. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

    NASA Astrophysics Data System (ADS)

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-01

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

  1. Deep convolutional neural networks for classifying GPR B-scans

    NASA Astrophysics Data System (ADS)

    Besaw, Lance E.; Stimac, Philip J.

    2015-05-01

    Symmetric and asymmetric buried explosive hazards (BEHs) present real, persistent, deadly threats on the modern battlefield. Current approaches to mitigate these threats rely on highly trained operatives to reliably detect BEHs with reasonable false alarm rates using handheld Ground Penetrating Radar (GPR) and metal detectors. As computers become smaller, faster and more efficient, there exists greater potential for automated threat detection based on state-of-the-art machine learning approaches, reducing the burden on the field operatives. Recent advancements in machine learning, specifically deep learning artificial neural networks, have led to significantly improved performance in pattern recognition tasks, such as object classification in digital images. Deep convolutional neural networks (CNNs) are used in this work to extract meaningful signatures from 2-dimensional (2-D) GPR B-scans and classify threats. The CNNs skip the traditional "feature engineering" step often associated with machine learning, and instead learn the feature representations directly from the 2-D data. A multi-antennae, handheld GPR with centimeter-accurate positioning data was used to collect shallow subsurface data over prepared lanes containing a wide range of BEHs. Several heuristics were used to prevent over-training, including cross validation, network weight regularization, and "dropout." Our results show that CNNs can extract meaningful features and accurately classify complex signatures contained in GPR B-scans, complementing existing GPR feature extraction and classification techniques.

  2. Method for Veterbi decoding of large constraint length convolutional codes

    NASA Astrophysics Data System (ADS)

    Hsu, In-Shek; Truong, Trieu-Kie; Reed, Irving S.; Jing, Sun

    1988-05-01

    A new method of Viterbi decoding of convolutional codes lends itself to a pipline VLSI architecture using a single sequential processor to compute the path metrics in the Viterbi trellis. An array method is used to store the path information for NK intervals where N is a number, and K is constraint length. The selected path at the end of each NK interval is then selected from the last entry in the array. A trace-back method is used for returning to the beginning of the selected path back, i.e., to the first time unit of the interval NK to read out the stored branch metrics of the selected path which correspond to the message bits. The decoding decision made in this way is no longer maximum likelihood, but can be almost as good, provided that constraint length K in not too small. The advantage is that for a long message, it is not necessary to provide a large memory to store the trellis derived information until the end of the message to select the path that is to be decoded; the selection is made at the end of every NK time unit, thus decoding a long message in successive blocks.

  3. A quantum algorithm for Viterbi decoding of classical convolutional codes

    NASA Astrophysics Data System (ADS)

    Grice, Jon R.; Meyer, David A.

    2015-07-01

    We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper, the proposed algorithm is applied to decoding classical convolutional codes, for instance, large constraint length and short decode frames . Other applications of the classical Viterbi algorithm where is large (e.g., speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butterfly diagram of the fast Fourier transform, with its corresponding fast quantum algorithm. The tensor-product structure of the butterfly diagram corresponds to a quantum superposition that we show can be efficiently prepared. The quantum speedup is possible because the performance of the QVA depends on the fanout (number of possible transitions from any given state in the hidden Markov model) which is in general much less than . The QVA constructs a superposition of states which correspond to all legal paths through the decoding lattice, with phase as a function of the probability of the path being taken given received data. A specialized amplitude amplification procedure is applied one or more times to recover a superposition where the most probable path has a high probability of being measured.

  4. Designing the optimal convolution kernel for modeling the motion blur

    NASA Astrophysics Data System (ADS)

    Jelinek, Jan

    2011-06-01

    Motion blur acts on an image like a two dimensional low pass filter, whose spatial frequency characteristic depends both on the trajectory of the relative motion between the scene and the camera and on the velocity vector variation along it. When motion during exposure is permitted, the conventional, static notions of both the image exposure and the scene-toimage mapping become unsuitable and must be revised to accommodate the image formation dynamics. This paper develops an exact image formation model for arbitrary object-camera relative motion with arbitrary velocity profiles. Moreover, for any motion the camera may operate in either continuous or flutter shutter exposure mode. Its result is a convolution kernel, which is optimally designed for both the given motion and sensor array geometry, and hence permits the most accurate computational undoing of the blurring effects for the given camera required in forensic and high security applications. The theory has been implemented and a few examples are shown in the paper.

  5. Method for Veterbi decoding of large constraint length convolutional codes

    NASA Technical Reports Server (NTRS)

    Hsu, In-Shek (Inventor); Truong, Trieu-Kie (Inventor); Reed, Irving S. (Inventor); Jing, Sun (Inventor)

    1988-01-01

    A new method of Viterbi decoding of convolutional codes lends itself to a pipline VLSI architecture using a single sequential processor to compute the path metrics in the Viterbi trellis. An array method is used to store the path information for NK intervals where N is a number, and K is constraint length. The selected path at the end of each NK interval is then selected from the last entry in the array. A trace-back method is used for returning to the beginning of the selected path back, i.e., to the first time unit of the interval NK to read out the stored branch metrics of the selected path which correspond to the message bits. The decoding decision made in this way is no longer maximum likelihood, but can be almost as good, provided that constraint length K in not too small. The advantage is that for a long message, it is not necessary to provide a large memory to store the trellis derived information until the end of the message to select the path that is to be decoded; the selection is made at the end of every NK time unit, thus decoding a long message in successive blocks.

  6. A convolution model for computing the far-field directivity of a parametric loudspeaker array.

    PubMed

    Shi, Chuang; Kajikawa, Yoshinobu

    2015-02-01

    This paper describes a method to compute the far-field directivity of a parametric loudspeaker array (PLA), whereby the steerable parametric loudspeaker can be implemented when phased array techniques are applied. The convolution of the product directivity and the Westervelt's directivity is suggested, substituting for the past practice of using the product directivity only. Computed directivity of a PLA using the proposed convolution model achieves significant improvement in agreement to measured directivity at a negligible computational cost.

  7. Minimal-memory realization of pearl-necklace encoders of general quantum convolutional codes

    SciTech Connect

    Houshmand, Monireh; Hosseini-Khayat, Saied

    2011-02-15

    Quantum convolutional codes, like their classical counterparts, promise to offer higher error correction performance than block codes of equivalent encoding complexity, and are expected to find important applications in reliable quantum communication where a continuous stream of qubits is transmitted. Grassl and Roetteler devised an algorithm to encode a quantum convolutional code with a ''pearl-necklace'' encoder. Despite their algorithm's theoretical significance as a neat way of representing quantum convolutional codes, it is not well suited to practical realization. In fact, there is no straightforward way to implement any given pearl-necklace structure. This paper closes the gap between theoretical representation and practical implementation. In our previous work, we presented an efficient algorithm to find a minimal-memory realization of a pearl-necklace encoder for Calderbank-Shor-Steane (CSS) convolutional codes. This work is an extension of our previous work and presents an algorithm for turning a pearl-necklace encoder for a general (non-CSS) quantum convolutional code into a realizable quantum convolutional encoder. We show that a minimal-memory realization depends on the commutativity relations between the gate strings in the pearl-necklace encoder. We find a realization by means of a weighted graph which details the noncommutative paths through the pearl necklace. The weight of the longest path in this graph is equal to the minimal amount of memory needed to implement the encoder. The algorithm has a polynomial-time complexity in the number of gate strings in the pearl-necklace encoder.

  8. Intraocular lens fabrication

    DOEpatents

    Salazar, Mike A.; Foreman, Larry R.

    1997-01-01

    This invention describes a method for fabricating an intraocular lens made rom clear Teflon.TM., Mylar.TM., or other thermoplastic material having a thickness of about 0.025 millimeters. These plastic materials are thermoformable and biocompatable with the human eye. The two shaped lenses are bonded together with a variety of procedures which may include thermosetting and solvent based adhesives, laser and impulse welding, and ultrasonic bonding. The fill tube, which is used to inject a refractive filling material is formed with the lens so as not to damage the lens shape. A hypodermic tube may be included inside the fill tube.

  9. Intraocular lens fabrication

    DOEpatents

    Salazar, M.A.; Foreman, L.R.

    1997-07-08

    This invention describes a method for fabricating an intraocular lens made from clear Teflon{trademark}, Mylar{trademark}, or other thermoplastic material having a thickness of about 0.025 millimeters. These plastic materials are thermoformable and biocompatable with the human eye. The two shaped lenses are bonded together with a variety of procedures which may include thermosetting and solvent based adhesives, laser and impulse welding, and ultrasonic bonding. The fill tube, which is used to inject a refractive filling material is formed with the lens so as not to damage the lens shape. A hypodermic tube may be included inside the fill tube. 13 figs.

  10. Fabric fastenings

    NASA Technical Reports Server (NTRS)

    Walen, E D; Fisher, R T

    1920-01-01

    The study of aeronautical fabrics has led to a consideration of the best methods of attaching and fastening together such materials. This report presents the results of an investigation upon the proper methods of attaching fabrics to airplane wings. The methods recommended in this report have been adopted by the military services.

  11. Text-Attentional Convolutional Neural Network for Scene Text Detection.

    PubMed

    He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian

    2016-06-01

    Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature globally computed from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this paper, we present a new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/non-text information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates the main task of text/non-text classification. In addition, a powerful low-level detector called contrast-enhancement maximally stable extremal regions (MSERs) is developed, which extends the widely used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 data set, with an F-measure of 0.82, substantially improving the state-of-the-art results. PMID:27093723

  12. A new model of the distal convoluted tubule.

    PubMed

    Ko, Benjamin; Mistry, Abinash C; Hanson, Lauren; Mallick, Rickta; Cooke, Leslie L; Hack, Bradley K; Cunningham, Patrick; Hoover, Robert S

    2012-09-01

    The Na(+)-Cl(-) cotransporter (NCC) in the distal convoluted tubule (DCT) of the kidney is a key determinant of Na(+) balance. Disturbances in NCC function are characterized by disordered volume and blood pressure regulation. However, many details concerning the mechanisms of NCC regulation remain controversial or undefined. This is partially due to the lack of a mammalian cell model of the DCT that is amenable to functional assessment of NCC activity. Previously reported investigations of NCC regulation in mammalian cells have either not attempted measurements of NCC function or have required perturbation of the critical without a lysine kinase (WNK)/STE20/SPS-1-related proline/alanine-rich kinase regulatory pathway before functional assessment. Here, we present a new mammalian model of the DCT, the mouse DCT15 (mDCT15) cell line. These cells display native NCC function as measured by thiazide-sensitive, Cl(-)-dependent (22)Na(+) uptake and allow for the separate assessment of NCC surface expression and activity. Knockdown by short interfering RNA confirmed that this function was dependent on NCC protein. Similar to the mammalian DCT, these cells express many of the known regulators of NCC and display significant baseline activity and dimerization of NCC. As described in previous models, NCC activity is inhibited by appropriate concentrations of thiazides, and phorbol esters strongly suppress function. Importantly, they display release of WNK4 inhibition of NCC by small hairpin RNA knockdown. We feel that this new model represents a critical tool for the study of NCC physiology. The work that can be accomplished in such a system represents a significant step forward toward unraveling the complex regulation of NCC.

  13. A convolution-superposition dose calculation engine for GPUs

    SciTech Connect

    Hissoiny, Sami; Ozell, Benoit; Despres, Philippe

    2010-03-15

    Purpose: Graphic processing units (GPUs) are increasingly used for scientific applications, where their parallel architecture and unprecedented computing power density can be exploited to accelerate calculations. In this paper, a new GPU implementation of a convolution/superposition (CS) algorithm is presented. Methods: This new GPU implementation has been designed from the ground-up to use the graphics card's strengths and to avoid its weaknesses. The CS GPU algorithm takes into account beam hardening, off-axis softening, kernel tilting, and relies heavily on raytracing through patient imaging data. Implementation details are reported as well as a multi-GPU solution. Results: An overall single-GPU acceleration factor of 908x was achieved when compared to a nonoptimized version of the CS algorithm implemented in PlanUNC in single threaded central processing unit (CPU) mode, resulting in approximatively 2.8 s per beam for a 3D dose computation on a 0.4 cm grid. A comparison to an established commercial system leads to an acceleration factor of approximately 29x or 0.58 versus 16.6 s per beam in single threaded mode. An acceleration factor of 46x has been obtained for the total energy released per mass (TERMA) calculation and a 943x acceleration factor for the CS calculation compared to PlanUNC. Dose distributions also have been obtained for a simple water-lung phantom to verify that the implementation gives accurate results. Conclusions: These results suggest that GPUs are an attractive solution for radiation therapy applications and that careful design, taking the GPU architecture into account, is critical in obtaining significant acceleration factors. These results potentially can have a significant impact on complex dose delivery techniques requiring intensive dose calculations such as intensity-modulated radiation therapy (IMRT) and arc therapy. They also are relevant for adaptive radiation therapy where dose results must be obtained rapidly.

  14. Text-Attentional Convolutional Neural Network for Scene Text Detection.

    PubMed

    He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian

    2016-06-01

    Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature globally computed from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this paper, we present a new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/non-text information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates the main task of text/non-text classification. In addition, a powerful low-level detector called contrast-enhancement maximally stable extremal regions (MSERs) is developed, which extends the widely used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 data set, with an F-measure of 0.82, substantially improving the state-of-the-art results.

  15. A staggered-grid convolutional differentiator for elastic wave modelling

    NASA Astrophysics Data System (ADS)

    Sun, Weijia; Zhou, Binzhong; Fu, Li-Yun

    2015-11-01

    The computation of derivatives in governing partial differential equations is one of the most investigated subjects in the numerical simulation of physical wave propagation. An analytical staggered-grid convolutional differentiator (CD) for first-order velocity-stress elastic wave equations is derived in this paper by inverse Fourier transformation of the band-limited spectrum of a first derivative operator. A taper window function is used to truncate the infinite staggered-grid CD stencil. The truncated CD operator is almost as accurate as the analytical solution, and as efficient as the finite-difference (FD) method. The selection of window functions will influence the accuracy of the CD operator in wave simulation. We search for the optimal Gaussian windows for different order CDs by minimizing the spectral error of the derivative and comparing the windows with the normal Hanning window function for tapering the CD operators. It is found that the optimal Gaussian window appears to be similar to the Hanning window function for tapering the same CD operator. We investigate the accuracy of the windowed CD operator and the staggered-grid FD method with different orders. Compared to the conventional staggered-grid FD method, a short staggered-grid CD operator achieves an accuracy equivalent to that of a long FD operator, with lower computational costs. For example, an 8th order staggered-grid CD operator can achieve the same accuracy of a 16th order staggered-grid FD algorithm but with half of the computational resources and time required. Numerical examples from a homogeneous model and a crustal waveguide model are used to illustrate the superiority of the CD operators over the conventional staggered-grid FD operators for the simulation of wave propagations.

  16. Deep convolutional networks for pancreas segmentation in CT imaging

    NASA Astrophysics Data System (ADS)

    Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

    2015-03-01

    Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

  17. Text-Attentional Convolutional Neural Network for Scene Text Detection

    NASA Astrophysics Data System (ADS)

    He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian

    2016-06-01

    Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this work, we present a new system for scene text detection by proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/nontext information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates main task of text/non-text classification. In addition, a powerful low-level detector called Contrast- Enhancement Maximally Stable Extremal Regions (CE-MSERs) is developed, which extends the widely-used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 dataset, with a F-measure of 0.82, improving the state-of-the-art results substantially.

  18. A convolutional neural network approach for objective video quality assessment.

    PubMed

    Le Callet, Patrick; Viard-Gaudin, Christian; Barba, Dominique

    2006-09-01

    This paper describes an application of neural networks in the field of objective measurement method designed to automatically assess the perceived quality of digital videos. This challenging issue aims to emulate human judgment and to replace very complex and time consuming subjective quality assessment. Several metrics have been proposed in literature to tackle this issue. They are based on a general framework that combines different stages, each of them addressing complex problems. The ambition of this paper is not to present a global perfect quality metric but rather to focus on an original way to use neural networks in such a framework in the context of reduced reference (RR) quality metric. Especially, we point out the interest of such a tool for combining features and pooling them in order to compute quality scores. The proposed approach solves some problems inherent to objective metrics that should predict subjective quality score obtained using the single stimulus continuous quality evaluation (SSCQE) method. This latter has been adopted by video quality expert group (VQEG) in its recently finalized reduced referenced and no reference (RRNR-TV) test plan. The originality of such approach compared to previous attempts to use neural networks for quality assessment, relies on the use of a convolutional neural network (CNN) that allows a continuous time scoring of the video. Objective features are extracted on a frame-by-frame basis on both the reference and the distorted sequences; they are derived from a perceptual-based representation and integrated along the temporal axis using a time-delay neural network (TDNN). Experiments conducted on different MPEG-2 videos, with bit rates ranging 2-6 Mb/s, show the effectiveness of the proposed approach to get a plausible model of temporal pooling from the human vision system (HVS) point of view. More specifically, a linear correlation criteria, between objective and subjective scoring, up to 0.92 has been obtained on

  19. Single-trial EEG RSVP classification using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William

    2016-05-01

    Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.

  20. Low cost damage tolerant composite fabrication

    NASA Technical Reports Server (NTRS)

    Palmer, R. J.; Freeman, W. T.

    1988-01-01

    The resin transfer molding (RTM) process applied to composite aircraft parts offers the potential for using low cost resin systems with dry graphite fabrics that can be significantly less expensive than prepreg tape fabricated components. Stitched graphite fabric composites have demonstrated compression after impact failure performance that equals or exceeds that of thermoplastic or tough thermoset matrix composites. This paper reviews methods developed to fabricate complex shape composite parts using stitched graphite fabrics to increase damage tolerance with RTM processes to reduce fabrication cost.

  1. Dose calculation of megavoltage IMRT using convolution kernels extracted from GafChromic EBT film-measured pencil beam profiles

    NASA Astrophysics Data System (ADS)

    Naik, Mehul S.

    Intensity-modulated radiation therapy (IMRT) is a 3D conformal radiation therapy technique that utilizes either a multileaf intensity-modulating collimator (MIMiC used with the NOMOS Peacock system) or a multileaf collimator (MLC) on a conventional linear accelerator for beam intensity modulation to afford increased conformity in dose distributions. Due to the high-dose gradient regions that are effectively created, particular emphasis should be placed in the accurate determination of pencil beam kernels that are utilized by pencil beam convolution algorithms employed by a number of commercial IMRT treatment planning systems (TPS). These kernels are determined from relatively large field dose profiles that are typically collected using an ion chamber during commissioning of the TPS, while recent studies have demonstrated improvements in dose calculation accuracy when incorporating film data into the commissioning measurements. For this study, it has been proposed that the shape of high-resolution dose kernels can be extracted directly from single pencil beam (beamlet) profile measurements acquired using high-precision dosimetric film in order to accurately compute dose distributions, specifically for small fields and the penumbra regions of the larger fields. The effectiveness of GafChromic EBT film as an appropriate dosimeter to acquire the necessary measurements was evaluated and compared to the conventional silver-halide Kodak EDR2 film. Using the NOMOS Peacock system, similar dose kernels were extracted through deconvolution of the elementary pencil beam profiles using the two different types of films. Independent convolution-based calculations were performed using these kernels, resulting in better agreement with the measured relative dose profiles, as compared to those determined by CORVUS TPS' finite-size pencil beam (FSPB) algorithm. Preliminary evaluation of the proposed method in performing kernel extraction for an MLC-based IMRT system also showed

  2. Convolution effect on TCR log response curve and the correction method for it

    NASA Astrophysics Data System (ADS)

    Chen, Q.; Liu, L. J.; Gao, J.

    2016-09-01

    Through-casing resistivity (TCR) logging has been successfully used in production wells for the dynamic monitoring of oil pools and the distribution of the residual oil, but its vertical resolution has limited its efficiency in identification of thin beds. The vertical resolution is limited by the distortion phenomenon of vertical response of TCR logging. The distortion phenomenon was studied in this work. It was found that the vertical response curve of TCR logging is the convolution of the true formation resistivity and the convolution function of TCR logging tool. Due to the effect of convolution, the measurement error at thin beds can reach 30% or even bigger. Thus the information of thin bed might be covered up very likely. The convolution function of TCR logging tool was obtained in both continuous and discrete way in this work. Through modified Lyle-Kalman deconvolution method, the true formation resistivity can be optimally estimated, so this inverse algorithm can correct the error caused by the convolution effect. Thus it can improve the vertical resolution of TCR logging tool for identification of thin beds.

  3. Self-shaping of bioinspired chiral composites

    NASA Astrophysics Data System (ADS)

    Rong, Qing-Qing; Cui, Yu-Hong; Shimada, Takahiro; Wang, Jian-Shan; Kitamura, Takayuki

    2014-08-01

    Self-shaping materials such as shape memory polymers have recently drawn considerable attention owing to their high shape-changing ability in response to changes in ambient conditions, and thereby have promising applications in the biomedical, biosensing, soft robotics and aerospace fields. Their design is a crucial issue of both theoretical and technological interest. Motivated by the shape-changing ability of Towel Gourd tendril helices during swelling/deswelling, we present a strategy for realizing self-shaping function through the deformation of micro/nanohelices. To guide the design and fabrication of self-shaping materials, the shape equations of bent configurations, twisted belts, and helices of slender chiral composite are developed using the variation method. Furthermore, it is numerically shown that the shape changes of a chiral composite can be tuned by the deformation of micro/nanohelices and the fabricated fiber directions. This work paves a new way to create self-shaping composites.

  4. Improving Ship Detection with Polarimetric SAR based on Convolution between Co-polarization Channels.

    PubMed

    Li, Haiyan; He, Yijun; Wang, Wenguang

    2009-01-01

    The convolution between co-polarization amplitude only data is studied to improve ship detection performance. The different statistical behaviors of ships and surrounding ocean are characterized a by two-dimensional convolution function (2D-CF) between different polarization channels. The convolution value of the ocean decreases relative to initial data, while that of ships increases. Therefore the contrast of ships to ocean is increased. The opposite variation trend of ocean and ships can distinguish the high intensity ocean clutter from ships' signatures. The new criterion can generally avoid mistaken detection by a constant false alarm rate detector. Our new ship detector is compared with other polarimetric approaches, and the results confirm the robustness of the proposed method.

  5. Modified convolution method to reconstruct particle hologram with an elliptical Gaussian beam illumination.

    PubMed

    Wu, Xuecheng; Wu, Yingchun; Yang, Jing; Wang, Zhihua; Zhou, Binwu; Gréhan, Gérard; Cen, Kefa

    2013-05-20

    Application of the modified convolution method to reconstruct digital inline holography of particle illuminated by an elliptical Gaussian beam is investigated. Based on the analysis on the formation of particle hologram using the Collins formula, the convolution method is modified to compensate the astigmatism by adding two scaling factors. Both simulated and experimental holograms of transparent droplets and opaque particles are used to test the algorithm, and the reconstructed images are compared with that using FRFT reconstruction. Results show that the modified convolution method can accurately reconstruct the particle image. This method has an advantage that the reconstructed images in different depth positions have the same size and resolution with the hologram. This work shows that digital inline holography has great potential in particle diagnostics in curvature containers.

  6. Fast vision through frameless event-based sensing and convolutional processing: application to texture recognition.

    PubMed

    Perez-Carrasco, Jose Antonio; Acha, Begona; Serrano, Carmen; Camunas-Mesa, Luis; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabe

    2010-04-01

    Address-event representation (AER) is an emergent hardware technology which shows a high potential for providing in the near future a solid technological substrate for emulating brain-like processing structures. When used for vision, AER sensors and processors are not restricted to capturing and processing still image frames, as in commercial frame-based video technology, but sense and process visual information in a pixel-level event-based frameless manner. As a result, vision processing is practically simultaneous to vision sensing, since there is no need to wait for sensing full frames. Also, only meaningful information is sensed, communicated, and processed. Of special interest for brain-like vision processing are some already reported AER convolutional chips, which have revealed a very high computational throughput as well as the possibility of assembling large convolutional neural networks in a modular fashion. It is expected that in a near future we may witness the appearance of large scale convolutional neural networks with hundreds or thousands of individual modules. In the meantime, some research is needed to investigate how to assemble and configure such large scale convolutional networks for specific applications. In this paper, we analyze AER spiking convolutional neural networks for texture recognition hardware applications. Based on the performance figures of already available individual AER convolution chips, we emulate large scale networks using a custom made event-based behavioral simulator. We have developed a new event-based processing architecture that emulates with AER hardware Manjunath's frame-based feature recognition software algorithm, and have analyzed its performance using our behavioral simulator. Recognition rate performance is not degraded. However, regarding speed, we show that recognition can be achieved before an equivalent frame is fully sensed and transmitted.

  7. Punctured Parallel and Serial Concatenated Convolutional Codes for BPSK/QPSK Channels

    NASA Technical Reports Server (NTRS)

    Acikel, Omer Fatih

    1999-01-01

    As available bandwidth for communication applications becomes scarce, bandwidth-efficient modulation and coding schemes become ever important. Since their discovery in 1993, turbo codes (parallel concatenated convolutional codes) have been the center of the attention in the coding community because of their bit error rate performance near the Shannon limit. Serial concatenated convolutional codes have also been shown to be as powerful as turbo codes. In this dissertation, we introduce algorithms for designing bandwidth-efficient rate r = k/(k + 1),k = 2, 3,..., 16, parallel and rate 3/4, 7/8, and 15/16 serial concatenated convolutional codes via puncturing for BPSK/QPSK (Binary Phase Shift Keying/Quadrature Phase Shift Keying) channels. Both parallel and serial concatenated convolutional codes have initially, steep bit error rate versus signal-to-noise ratio slope (called the -"cliff region"). However, this steep slope changes to a moderate slope with increasing signal-to-noise ratio, where the slope is characterized by the weight spectrum of the code. The region after the cliff region is called the "error rate floor" which dominates the behavior of these codes in moderate to high signal-to-noise ratios. Our goal is to design high rate parallel and serial concatenated convolutional codes while minimizing the error rate floor effect. The design algorithm includes an interleaver enhancement procedure and finds the polynomial sets (only for parallel concatenated convolutional codes) and the puncturing schemes that achieve the lowest bit error rate performance around the floor for the code rates of interest.

  8. Calculation of reflectance distribution using angular spectrum convolution in mesh-based computer generated hologram.

    PubMed

    Yeom, Han-Ju; Park, Jae-Hyeung

    2016-08-22

    We propose a method to obtain a computer-generated hologram that renders reflectance distributions of individual mesh surfaces of three-dimensional objects. Unlike previous methods which find phase distribution inside each mesh, the proposed method performs convolution of angular spectrum of the mesh to obtain desired reflectance distribution. Manipulation in the angular spectrum domain enables its application to fully-analytic mesh based computer generated hologram, removing the necessity for resampling of the spatial frequency grid. It is also computationally inexpensive as the convolution can be performed efficiently using Fourier transform. In this paper, we present principle, error analysis, simulation, and experimental verification results of the proposed method.

  9. Transfer Function Bounds for Partial-unit-memory Convolutional Codes Based on Reduced State Diagram

    NASA Technical Reports Server (NTRS)

    Lee, P. J.

    1984-01-01

    The performance of a coding system consisting of a convolutional encoder and a Viterbi decoder is analytically found by the well-known transfer function bounding technique. For the partial-unit-memory byte-oriented convolutional encoder with m sub 0 binary memory cells and (k sub 0 m sub 0) inputs, a state diagram of 2(K) (sub 0) was for the transfer function bound. A reduced state diagram of (2 (m sub 0) +1) is used for easy evaluation of transfer function bounds for partial-unit-memory codes.

  10. Blind separation of convolutive sEMG mixtures based on independent vector analysis

    NASA Astrophysics Data System (ADS)

    Wang, Xiaomei; Guo, Yina; Tian, Wenyan

    2015-12-01

    An independent vector analysis (IVA) method base on variable-step gradient algorithm is proposed in this paper. According to the sEMG physiological properties, the IVA model is applied to the frequency-domain separation of convolutive sEMG mixtures to extract motor unit action potentials information of sEMG signals. The decomposition capability of proposed method is compared to the one of independent component analysis (ICA), and experimental results show the variable-step gradient IVA method outperforms ICA in blind separation of convolutive sEMG mixtures.

  11. A convolutional learning system for object classification in 3-D Lidar data.

    PubMed

    Prokhorov, Danil

    2010-05-01

    In this brief, a convolutional learning system for classification of segmented objects represented in 3-D as point clouds of laser reflections is proposed. Several novelties are discussed: (1) extension of the existing convolutional neural network (CNN) framework to direct processing of 3-D data in a multiview setting which may be helpful for rotation-invariant consideration, (2) improvement of CNN training effectiveness by employing a stochastic meta-descent (SMD) method, and (3) combination of unsupervised and supervised training for enhanced performance of CNN. CNN performance is illustrated on a two-class data set of objects in a segmented outdoor environment.

  12. Patient-specific dosimetry based on quantitative SPECT imaging and 3D-DFT convolution

    SciTech Connect

    Akabani, G.; Hawkins, W.G.; Eckblade, M.B.; Leichner, P.K.

    1999-01-01

    The objective of this study was to validate the use of a 3-D discrete Fourier Transform (3D-DFT) convolution method to carry out the dosimetry for I-131 for soft tissues in radioimmunotherapy procedures. To validate this convolution method, mathematical and physical phantoms were used as a basis of comparison with Monte Carlo transport (MCT) calculations which were carried out using the EGS4 system code. The mathematical phantom consisted of a sphere containing uniform and nonuniform activity distributions. The physical phantom consisted of a cylinder containing uniform and nonuniform activity distributions. Quantitative SPECT reconstruction was carried out using the Circular Harmonic Transform (CHT) algorithm.

  13. Directed light fabrication

    NASA Astrophysics Data System (ADS)

    Lewis, G. K.; Nemec, R.; Milewski, J.; Thoma, D. J.; Cremers, D.; Barbe, M.

    1994-09-01

    Directed Light Fabrication (DLF) is a rapid prototyping process being developed at Los Alamos National Laboratory to fabricate metal components. This is done by fusing gas delivered metal powder particles in the focal zone of a laser beam that is programmed to move along or across the part cross section. Fully dense metal is built up a layer at a time to form the desired part represented by a 3 dimensional solid model from CAD software. Machine 'tool paths' are created from the solid model that command the movement and processing parameters specific to the DLF process so that the part can be built one layer at a time. The result is a fully dense, near net shape metal part that solidifies under rapid solidification conditions.

  14. Fabrication of metallic glass structures

    DOEpatents

    Cline, Carl F.

    1986-01-01

    Amorphous metal powders or ribbons are fabricated into solid shapes of appreciable thickness by the application of compaction energy. The temperature regime wherein the amorphous metal deforms by viscous flow is measured. The metal powders or ribbons are compacted within the temperature range.

  15. Fabrication of metallic glass structures

    DOEpatents

    Cline, C.F.

    1983-10-20

    Amorphous metal powders or ribbons are fabricated into solid shapes of appreciable thickness by the application of compaction energy. The temperature regime wherein the amorphous metal deforms by viscous flow is measured. The metal powders or ribbons are compacted within the temperature regime.

  16. Virtual reflector multidimensional deconvolution: inversion issues for convolutive-type interferometry

    NASA Astrophysics Data System (ADS)

    Poletto, F.; Bellezza, C.; Farina, B.

    2014-02-01

    We examine the multidimensional deconvolution (MDD) approach for virtual-reflector (VR) signal representation by cross-convolution. Assuming wavefield separation at receivers, the VR signal can be synthesized by cross-convolution of inward and outward wavefields generated from a multiplicity of transient sources. Under suitable conditions, this virtual signal is representable as the multidimensional composition of (1) the outward wavefield from the redatumed virtual sources at receivers and (2) of the so-called point-spread function (PSF) for VRs. Multidimensional inversion of the PSF provides the solution to get deblurred signals and recover the Green's function either of transmitted wavefields or reflectivity. This approach is similar to the MDD by backward seismic interferometry of cross-correlation type. The forward approach by cross-convolution poses the issue to use suitable projections and representations by functions with convex trends in space and time. This work discusses the main differences for illumination and stability in the cross-convolution and cross-correlation approaches, providing, under appropriate coverage conditions, equivalent and robust inversion results.

  17. The VLSI design of an error-trellis syndrome decoder for certain convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Jensen, J. M.; Hsu, I.-S.; Truong, T. K.

    1986-01-01

    A recursive algorithm using the error-trellis decoding technique is developed to decode convolutional codes (CCs). An example, illustrating the very large scale integration (VLSI) architecture of such a decode, is given for a dual-K CC. It is demonstrated that such a decoder can be realized readily on a single chip with metal-nitride-oxide-semiconductor technology.

  18. The VLSI design of error-trellis syndrome decoding for convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Jensen, J. M.; Truong, T. K.; Hsu, I. S.

    1985-01-01

    A recursive algorithm using the error-trellis decoding technique is developed to decode convolutional codes (CCs). An example, illustrating the very large scale integration (VLSI) architecture of such a decode, is given for a dual-K CC. It is demonstrated that such a decoder can be realized readily on a single chip with metal-nitride-oxide-semiconductor technology.

  19. Profile of CT scan output dose in axial and helical modes using convolution

    NASA Astrophysics Data System (ADS)

    Anam, C.; Haryanto, F.; Widita, R.; Arif, I.; Dougherty, G.

    2016-03-01

    The profile of the CT scan output dose is crucial for establishing the patient dose profile. The purpose of this study is to investigate the profile of the CT scan output dose in both axial and helical modes using convolution. A single scan output dose profile (SSDP) in the center of a head phantom was measured using a solid-state detector. The multiple scan output dose profile (MSDP) in the axial mode was calculated using convolution between SSDP and delta function, whereas for the helical mode MSDP was calculated using convolution between SSDP and the rectangular function. MSDPs were calculated for a number of scans (5, 10, 15, 20 and 25). The multiple scan average dose (MSAD) for differing numbers of scans was compared to the value of CT dose index (CTDI). Finally, the edge values of MSDP for every scan number were compared to the corresponding MSAD values. MSDPs were successfully generated by using convolution between a SSDP and the appropriate function. We found that CTDI only accurately estimates MSAD when the number of scans was more than 10. We also found that the edge values of the profiles were 42% to 93% lower than that the corresponding MSADs.

  20. The multipoint de la Vallee-Poussin problem for a convolution operator

    SciTech Connect

    Napalkov, Valentin V; Nuyatov, Andrey A

    2012-02-28

    Conditions are discovered which ensure that the space of entire functions can be represented as the sum of an ideal in the space of entire functions and the kernel of a convolution operator. In this way conditions for the multipoint de la Vallee-Poussin problem to have a solution are found. Bibliography: 14 titles.

  1. A generalized recursive convolution method for time-domain propagation in porous media.

    PubMed

    Dragna, Didier; Pineau, Pierre; Blanc-Benon, Philippe

    2015-08-01

    An efficient numerical method, referred to as the auxiliary differential equation (ADE) method, is proposed to compute convolutions between relaxation functions and acoustic variables arising in sound propagation equations in porous media. For this purpose, the relaxation functions are approximated in the frequency domain by rational functions. The time variation of the convolution is thus governed by first-order differential equations which can be straightforwardly solved. The accuracy of the method is first investigated and compared to that of recursive convolution methods. It is shown that, while recursive convolution methods are first or second-order accurate in time, the ADE method does not introduce any additional error. The ADE method is then applied for outdoor sound propagation using the equations proposed by Wilson et al. in the ground [(2007). Appl. Acoust. 68, 173-200]. A first one-dimensional case is performed showing that only five poles are necessary to accurately approximate the relaxation functions for typical applications. Finally, the ADE method is used to compute sound propagation in a three-dimensional geometry over an absorbing ground. Results obtained with Wilson's equations are compared to those obtained with Zwikker and Kosten's equations and with an impedance surface for different flow resistivities.

  2. Convolutional neural network based sensor fusion for forward looking ground penetrating radar

    NASA Astrophysics Data System (ADS)

    Sakaguchi, Rayn; Crosskey, Miles; Chen, David; Walenz, Brett; Morton, Kenneth

    2016-05-01

    Forward looking ground penetrating radar (FLGPR) is an alternative buried threat sensing technology designed to offer additional standoff compared to downward looking GPR systems. Due to additional flexibility in antenna configurations, FLGPR systems can accommodate multiple sensor modalities on the same platform that can provide complimentary information. The different sensor modalities present challenges in both developing informative feature extraction methods, and fusing sensor information in order to obtain the best discrimination performance. This work uses convolutional neural networks in order to jointly learn features across two sensor modalities and fuse the information in order to distinguish between target and non-target regions. This joint optimization is possible by modifying the traditional image-based convolutional neural network configuration to extract data from multiple sources. The filters generated by this process create a learned feature extraction method that is optimized to provide the best discrimination performance when fused. This paper presents the results of applying convolutional neural networks and compares these results to the use of fusion performed with a linear classifier. This paper also compares performance between convolutional neural networks architectures to show the benefit of fusing the sensor information in different ways.

  3. The venom apparatus in stenogastrine wasps: subcellular features of the convoluted gland.

    PubMed

    Petrocelli, Iacopo; Turillazzi, Stefano; Delfino, Giovanni

    2014-09-01

    In the wasp venom apparatus, the convoluted gland is the tract of the thin secretory unit, i.e. filament, contained in the muscular reservoir. Previous transmission electron microscope investigation on Stenogastrinae disclosed that the free filaments consist of distal and proximal tracts, from/to the venom reservoir, characterized by class 3 and 2 gland patterns, respectively. This study aims to extend the ultrastructural analysis to the convoluted tract, in order to provide a thorough, subcellular representation of the venom gland in these Asian wasps. Our findings showed that the convoluted gland is a continuation of the proximal tract, with secretory cells provided with a peculiar apical invagination, the extracellular cavity, collecting their products. This compartment holds a simple end-apparatus lined by large and ramified microvilli that contribute to the processing of the secretory product. A comparison between previous and present findings reveals a noticeable regionalization of the stenogastrine venom filaments and suggests that the secretory product acquires its ultimate composition in the convoluted tract.

  4. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features.

    PubMed

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-10-01

    Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the performance

  5. Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

    NASA Astrophysics Data System (ADS)

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-03-01

    Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by

  6. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

    PubMed Central

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-01-01

    Abstract. Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the

  7. Fabrication of tungsten wire reinforced nickel-base alloy composites

    NASA Technical Reports Server (NTRS)

    Brentnall, W. D.; Toth, I. J.

    1974-01-01

    Fabrication methods for tungsten fiber reinforced nickel-base superalloy composites were investigated. Three matrix alloys in pre-alloyed powder or rolled sheet form were evaluated in terms of fabricability into composite monotape and multi-ply forms. The utility of monotapes for fabricating more complex shapes was demonstrated. Preliminary 1093C (2000F) stress rupture tests indicated that efficient utilization of fiber strength was achieved in composites fabricated by diffusion bonding processes. The fabrication of thermal fatigue specimens is also described.

  8. Fabrication Technology

    SciTech Connect

    Blaedel, K.L.

    1993-03-01

    The mission of the Fabrication Technology thrust area is to have an adequate base of manufacturing technology, not necessarily resident at Lawrence Livermore National Laboratory (LLNL), to conduct the future business of LLNL. The specific goals continue to be to (1) develop an understanding of fundamental fabrication processes; (2) construct general purpose process models that will have wide applicability; (3) document findings and models in journals; (4) transfer technology to LLNL programs, industry, and colleagues; and (5) develop continuing relationships with the industrial and academic communities to advance the collective understanding of fabrication processes. The strategy to ensure success is changing. For technologies in which they are expert and which will continue to be of future importance to LLNL, they can often attract outside resources both to maintain their expertise by applying it to a specific problem and to help fund further development. A popular vehicle to fund such work is the Cooperative Research and Development Agreement with industry. For technologies needing development because of their future critical importance and in which they are not expert, they use internal funding sources. These latter are the topics of the thrust area. Three FY-92 funded projects are discussed in this section. Each project clearly moves the Fabrication Technology thrust area towards the goals outlined above. They have also continued their membership in the North Carolina State University Precision Engineering Center, a multidisciplinary research and graduate program established to provide the new technologies needed by high-technology institutions in the US. As members, they have access to and use of the results of their research projects, many of which parallel the precision engineering efforts at LLNL.

  9. Determining micro- and macro- geometry of fabric and fabric reinforced composites

    NASA Astrophysics Data System (ADS)

    Huang, Lejian

    Textile composites are made from textile fabric and resin. Depending on the weaving pattern, composite reinforcements can be characterized into two groups: uniform fabric and near-net shape fabric. Uniform fabric can be treated as an assembly of its smallest repeating pattern also called a unit cell; the latter is a single component with complex structure. Due to advantages of cost savings and inherent toughness, near-net shape fabric has gained great success in composite industries, for application such as turbine blades. Mechanical properties of textile composites are mainly determined by the geometry of the composite reinforcements. The study of a composite needs a computational tool to link fabric micro- and macro-geometry with the textile weaving process and composite manufacturing process. A textile fabric consists of a number of yarns or tows, and each yarn is a bundle of fibers. In this research, a fiber-level approach known as the digital element approach (DEA) is adopted to model the micro- and macro-geometry of fabric and fabric reinforced composites. This approach determines fabric geometry based on textile weaving mechanics. A solver with a dynamic explicit algorithm is employed in the DEA. In modeling a uniform fabric, the topology of the fabric unit cell is first established based on the weaving pattern, followed by yarn discretization. An explicit algorithm with a periodic boundary condition is then employed during the simulation. After its detailed geometry is obtained, the unit cell is then assembled to yield a fabric micro-geometry. Fabric micro-geometry can be expressed at both fiber- and yarn-levels. In modeling a near-net shape fabric component, all theories used in simulating the uniform fabric are kept except the periodic boundary condition. Since simulating the entire component at the fiber-level requires a large amount of time and memory, parallel program is used during the simulation. In modeling a net-shape composite, a dynamic molding

  10. Fullerene embedded shape memory nanolens array.

    PubMed

    Jeon, Sohee; Jang, Jun Young; Youn, Jae Ryoun; Jeong, Jun-Ho; Brenner, Howard; Song, Young Seok

    2013-01-01

    Securing fragile nanostructures against external impact is indispensable for offering sufficiently long lifetime in service to nanoengineering products, especially when coming in contact with other substances. Indeed, this problem still remains a challenging task, which may be resolved with the help of smart materials such as shape memory and self-healing materials. Here, we demonstrate a shape memory nanostructure that can recover its shape by absorbing electromagnetic energy. Fullerenes were embedded into the fabricated nanolens array. Beside the energy absorption, such addition enables a remarkable enhancement in mechanical properties of shape memory polymer. The shape memory nanolens was numerically modeled to impart more in-depth understanding on the physics regarding shape recovery behavior of the fabricated nanolens. We anticipate that our strategy of combining the shape memory property with the microwave irradiation feature can provide a new pathway for nanostructured systems able to ensure a long-term durability. PMID:24253423

  11. Variable-shape solar-energy concentrator

    NASA Technical Reports Server (NTRS)

    Miller, C. G.; Phol, J. H.

    1979-01-01

    Proposed low cost three dimensional tracking solar concentrator fabricated from lightweight, flexible polymeric film membrane is controlled in shape by differential pressure loading. Fine adjustments to shape could be made by mounting electrets or magnets on membrane or applying electric or magnetic field.

  12. Two-level pipelined systolic array for multi-dimensional convolution

    SciTech Connect

    Kung, H.T.; Ruane, L.M.; Yen, D.W.L.

    1982-11-01

    This paper describes a systolic array for the computation of n-dimensional (n-D) convolutions of any positive integer n. Systolic systems usually achieve high performance by allowing computations to be pipelined over a large array of processing elements. To achieve even higher performance, the systolic array of this paper utilizes a second level of pipelining by allowing the processing elements themselves to be pipelined to an arbitrary degree. Moreover, it is shown that as far as orders of magnitude are concerned, the total amount of memory required by the systolic array is no more than that needed by any convolution device that reads in each input data item only once. Thus if only schemes that use the minimum-possible I/O are considered, the systolic array is not only high performance, but also optimal in terms of the amount of required memory.

  13. Systolic array architecture for convolutional decoding algorithms: Viterbi algorithm and stack algorithm

    SciTech Connect

    Chang, C.Y.

    1986-01-01

    New results on efficient forms of decoding convolutional codes based on Viterbi and stack algorithms using systolic array architecture are presented. Some theoretical aspects of systolic arrays are also investigated. First, systolic array implementation of Viterbi algorithm is considered, and various properties of convolutional codes are derived. A technique called strongly connected trellis decoding is introduced to increase the efficient utilization of all the systolic array processors. The issues dealing with the composite branch metric generation, survivor updating, overall system architecture, throughput rate, and computations overhead ratio are also investigated. Second, the existing stack algorithm is modified and restated in a more concise version so that it can be efficiently implemented by a special type of systolic array called systolic priority queue. Three general schemes of systolic priority queue based on random access memory, shift register, and ripple register are proposed. Finally, a systematic approach is presented to design systolic arrays for certain general classes of recursively formulated algorithms.

  14. Automatic detection of cell divisions (mitosis) in live-imaging microscopy images using Convolutional Neural Networks.

    PubMed

    Shkolyar, Anat; Gefen, Amit; Benayahu, Dafna; Greenspan, Hayit

    2015-08-01

    We propose a semi-automated pipeline for the detection of possible cell divisions in live-imaging microscopy and the classification of these mitosis candidates using a Convolutional Neural Network (CNN). We use time-lapse images of NIH3T3 scratch assay cultures, extract patches around bright candidate regions that then undergo segmentation and binarization, followed by a classification of the binary patches into either containing or not containing cell division. The classification is performed by training a Convolutional Neural Network on a specially constructed database. We show strong results of AUC = 0.91 and F-score = 0.89, competitive with state-of-the-art methods in this field. PMID:26736369

  15. Brain tumor grading based on Neural Networks and Convolutional Neural Networks.

    PubMed

    Yuehao Pan; Weimin Huang; Zhiping Lin; Wanzheng Zhu; Jiayin Zhou; Wong, Jocelyn; Zhongxiang Ding

    2015-08-01

    This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, with some combination operations between multiphase MRIs. Compared to other researches, which involve additional effort to design and choose feature sets, the approach used in this paper leverages the learning capability of deep learning machine. We present the grading performance on the testing data measured by the sensitivity and specificity. The results show a maximum improvement of 18% on grading performance of Convolutional Neural Networks based on sensitivity and specificity compared to Neural Networks. We also visualize the kernels trained in different layers and display some self-learned features obtained from Convolutional Neural Networks. PMID:26736358

  16. Automatic detection of cell divisions (mitosis) in live-imaging microscopy images using Convolutional Neural Networks.

    PubMed

    Shkolyar, Anat; Gefen, Amit; Benayahu, Dafna; Greenspan, Hayit

    2015-08-01

    We propose a semi-automated pipeline for the detection of possible cell divisions in live-imaging microscopy and the classification of these mitosis candidates using a Convolutional Neural Network (CNN). We use time-lapse images of NIH3T3 scratch assay cultures, extract patches around bright candidate regions that then undergo segmentation and binarization, followed by a classification of the binary patches into either containing or not containing cell division. The classification is performed by training a Convolutional Neural Network on a specially constructed database. We show strong results of AUC = 0.91 and F-score = 0.89, competitive with state-of-the-art methods in this field.

  17. Video-based convolutional neural networks for activity recognition from robot-centric videos

    NASA Astrophysics Data System (ADS)

    Ryoo, M. S.; Matthies, Larry

    2016-05-01

    In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

  18. Brain tumor grading based on Neural Networks and Convolutional Neural Networks.

    PubMed

    Yuehao Pan; Weimin Huang; Zhiping Lin; Wanzheng Zhu; Jiayin Zhou; Wong, Jocelyn; Zhongxiang Ding

    2015-08-01

    This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, with some combination operations between multiphase MRIs. Compared to other researches, which involve additional effort to design and choose feature sets, the approach used in this paper leverages the learning capability of deep learning machine. We present the grading performance on the testing data measured by the sensitivity and specificity. The results show a maximum improvement of 18% on grading performance of Convolutional Neural Networks based on sensitivity and specificity compared to Neural Networks. We also visualize the kernels trained in different layers and display some self-learned features obtained from Convolutional Neural Networks.

  19. Convolution theorem for the three-dimensional entangled fractional Fourier transformation deduced from the tripartite entangled state representation

    NASA Astrophysics Data System (ADS)

    Liu, Shu-Guang; Fan, Hong-Yi

    2009-12-01

    We find that constructing the two mutually-conjugate tripartite entangled state representations naturally leads to the entangled Fourier transformation. We then derive the convolution theorem for the threedimensional entangled fractional Fourier transformation in the context of quantum mechanics.

  20. Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition

    NASA Astrophysics Data System (ADS)

    Popko, E. A.; Weinstein, I. A.

    2016-08-01

    Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.

  1. Hardware accelerator of convolution with exponential function for image processing applications

    NASA Astrophysics Data System (ADS)

    Panchenko, Ivan; Bucha, Victor

    2015-12-01

    In this paper we describe a Hardware Accelerator (HWA) for fast recursive approximation of separable convolution with exponential function. This filter can be used in many Image Processing (IP) applications, e.g. depth-dependent image blur, image enhancement and disparity estimation. We have adopted this filter RTL implementation to provide maximum throughput in constrains of required memory bandwidth and hardware resources to provide a power-efficient VLSI implementation.

  2. Convolution-based estimation of organ dose in tube current modulated CT

    NASA Astrophysics Data System (ADS)

    Tian, Xiaoyu; Segars, W. P.; Dixon, R. L.; Samei, Ehsan

    2015-03-01

    Among the various metrics that quantify radiation dose in computed tomography (CT), organ dose is one of the most representative quantities reflecting patient-specific radiation burden.1 Accurate estimation of organ dose requires one to effectively model the patient anatomy and the irradiation field. As illustrated in previous studies, the patient anatomy factor can be modeled using a library of computational phantoms with representative body habitus.2 However, the modeling of irradiation field can be practically challenging, especially for CT exams performed with tube current modulation. The central challenge is to effectively quantify the scatter irradiation field created by the dynamic change of tube current. In this study, we present a convolution-based technique to effectively quantify the primary and scatter irradiation field for TCM examinations. The organ dose for a given clinical patient can then be rapidly determined using the convolution-based method, a patient-matching technique, and a library of computational phantoms. 58 adult patients were included in this study (age range: 18-70 y.o., weight range: 60-180 kg). One computational phantom was created based on the clinical images of each patient. Each patient was optimally matched against one of the remaining 57 computational phantoms using a leave-one-out strategy. For each computational phantom, the organ dose coefficients (CTDIvol-normalized organ dose) under fixed tube current were simulated using a validated Monte Carlo simulation program. Such organ dose coefficients were multiplied by a scaling factor, (CTDIvol )organ, convolution that quantifies the regional irradiation field. The convolution-based organ dose was compared with the organ dose simulated from Monte Carlo program with TCM profiles explicitly modeled on the original phantom created based on patient images. The estimation error was within 10% across all organs and modulation profiles for abdominopelvic examination. This strategy

  3. Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields.

    PubMed

    Liu, Fayao; Shen, Chunhua; Lin, Guosheng; Reid, Ian

    2016-10-01

    In this article, we tackle the problem of depth estimation from single monocular images. Compared with depth estimation using multiple images such as stereo depth perception, depth from monocular images is much more challenging. Prior work typically focuses on exploiting geometric priors or additional sources of information, most using hand-crafted features. Recently, there is mounting evidence that features from deep convolutional neural networks (CNN) set new records for various vision applications. On the other hand, considering the continuous characteristic of the depth values, depth estimation can be naturally formulated as a continuous conditional random field (CRF) learning problem. Therefore, here we present a deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF. In particular, we propose a deep structured learning scheme which learns the unary and pairwise potentials of continuous CRF in a unified deep CNN framework. We then further propose an equally effective model based on fully convolutional networks and a novel superpixel pooling method, which is about 10 times faster, to speedup the patch-wise convolutions in the deep model. With this more efficient model, we are able to design deeper networks to pursue better performance. Our proposed method can be used for depth estimation of general scenes with no geometric priors nor any extra information injected. In our case, the integral of the partition function can be calculated in a closed form such that we can exactly solve the log-likelihood maximization. Moreover, solving the inference problem for predicting depths of a test image is highly efficient as closed-form solutions exist. Experiments on both indoor and outdoor scene datasets demonstrate that the proposed method outperforms state-of-the-art depth estimation approaches. PMID:26660697

  4. Quantum Fields Obtained from Convoluted Generalized White Noise Never Have Positive Metric

    NASA Astrophysics Data System (ADS)

    Albeverio, Sergio; Gottschalk, Hanno

    2016-05-01

    It is proven that the relativistic quantum fields obtained from analytic continuation of convoluted generalized (Lévy type) noise fields have positive metric, if and only if the noise is Gaussian. This follows as an easy observation from a criterion by Baumann, based on the Dell'Antonio-Robinson-Greenberg theorem, for a relativistic quantum field in positive metric to be a free field.

  5. Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields.

    PubMed

    Liu, Fayao; Shen, Chunhua; Lin, Guosheng; Reid, Ian

    2016-10-01

    In this article, we tackle the problem of depth estimation from single monocular images. Compared with depth estimation using multiple images such as stereo depth perception, depth from monocular images is much more challenging. Prior work typically focuses on exploiting geometric priors or additional sources of information, most using hand-crafted features. Recently, there is mounting evidence that features from deep convolutional neural networks (CNN) set new records for various vision applications. On the other hand, considering the continuous characteristic of the depth values, depth estimation can be naturally formulated as a continuous conditional random field (CRF) learning problem. Therefore, here we present a deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF. In particular, we propose a deep structured learning scheme which learns the unary and pairwise potentials of continuous CRF in a unified deep CNN framework. We then further propose an equally effective model based on fully convolutional networks and a novel superpixel pooling method, which is about 10 times faster, to speedup the patch-wise convolutions in the deep model. With this more efficient model, we are able to design deeper networks to pursue better performance. Our proposed method can be used for depth estimation of general scenes with no geometric priors nor any extra information injected. In our case, the integral of the partition function can be calculated in a closed form such that we can exactly solve the log-likelihood maximization. Moreover, solving the inference problem for predicting depths of a test image is highly efficient as closed-form solutions exist. Experiments on both indoor and outdoor scene datasets demonstrate that the proposed method outperforms state-of-the-art depth estimation approaches.

  6. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

    PubMed Central

    Yang, Xinyi

    2016-01-01

    In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. PMID:27610128

  7. Hardware efficient implementation of DFT using an improved first-order moments based cyclic convolution structure

    NASA Astrophysics Data System (ADS)

    Xiong, Jun; Liu, J. G.; Cao, Li

    2015-12-01

    This paper presents hardware efficient designs for implementing the one-dimensional (1D) discrete Fourier transform (DFT). Once DFT is formulated as the cyclic convolution form, the improved first-order moments-based cyclic convolution structure can be used as the basic computing unit for the DFT computation, which only contains a control module, a barrel shifter and (N-1)/2 accumulation units. After decomposing and reordering the twiddle factors, all that remains to do is shifting the input data sequence and accumulating them under the control of the statistical results on the twiddle factors. The whole calculation process only contains shift operations and additions with no need for multipliers and large memory. Compared with the previous first-order moments-based structure for DFT, the proposed designs have the advantages of less hardware consumption, lower power consumption and the flexibility to achieve better performance in certain cases. A series of experiments have proven the high performance of the proposed designs in terms of the area time product and power consumption. Similar efficient designs can be obtained for other computations, such as DCT/IDCT, DST/IDST, digital filter and correlation by transforming them into the forms of the first-order moments based cyclic convolution.

  8. Vehicle detection based on visual saliency and deep sparse convolution hierarchical model

    NASA Astrophysics Data System (ADS)

    Cai, Yingfeng; Wang, Hai; Chen, Xiaobo; Gao, Li; Chen, Long

    2016-07-01

    Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification. These types of methods generally have high processing times and low vehicle detection performance. To address this issue, a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed. A visual saliency calculation is firstly used to generate a small vehicle candidate area. The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection. The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group, which outperforms the existing state-of-the-art algorithms. More importantly, high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

  9. Vehicle detection based on visual saliency and deep sparse convolution hierarchical model

    NASA Astrophysics Data System (ADS)

    Cai, Yingfeng; Wang, Hai; Chen, Xiaobo; Gao, Li; Chen, Long

    2016-06-01

    Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification. These types of methods generally have high processing times and low vehicle detection performance. To address this issue, a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed. A visual saliency calculation is firstly used to generate a small vehicle candidate area. The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection. The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group, which outperforms the existing state-of-the-art algorithms. More importantly, high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

  10. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.

    PubMed

    Pang, Shan; Yang, Xinyi

    2016-01-01

    In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

  11. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

    PubMed Central

    Yang, Xinyi

    2016-01-01

    In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

  12. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.

    PubMed

    Pang, Shan; Yang, Xinyi

    2016-01-01

    In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. PMID:27610128

  13. Post polymerization cure shape memory polymers

    SciTech Connect

    Wilson, Thomas S; Hearon, Michael Keith; Bearinger, Jane P

    2014-11-11

    This invention relates to chemical polymer compositions, methods of synthesis, and fabrication methods for devices regarding polymers capable of displaying shape memory behavior (SMPs) and which can first be polymerized to a linear or branched polymeric structure, having thermoplastic properties, subsequently processed into a device through processes typical of polymer melts, solutions, and dispersions and then crossed linked to a shape memory thermoset polymer retaining the processed shape.

  14. A Cable-Shaped Lithium Sulfur Battery.

    PubMed

    Fang, Xin; Weng, Wei; Ren, Jing; Peng, Huisheng

    2016-01-20

    A carbon nanostructured hybrid fiber is developed by integrating mesoporous carbon and graphene oxide into aligned carbon nanotubes. This hybrid fiber is used as a 1D cathode to fabricate a new cable-shaped lithium-sulfur battery. The fiber cathode exhibits a decent specific capacity and lifespan, which makes the cable-shaped lithium-sulfur battery rank far ahead of other fiber-shaped batteries.

  15. Convolution-based estimation of organ dose in tube current modulated CT

    PubMed Central

    Tian, Xiaoyu; Segars, W Paul; Dixon, Robert L; Samei, Ehsan

    2016-01-01

    Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460–7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18–70 years, weight range: 60–180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients (hOrgan) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate (CTDIvol)organ, convolution values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying (CTDIvol)organ, convolution with the organ dose coefficients (hOrgan). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled. The discrepancy between the estimated organ dose and dose simulated using TCM Monte Carlo program was quantified. We further compared the

  16. Convolution-based estimation of organ dose in tube current modulated CT

    NASA Astrophysics Data System (ADS)

    Tian, Xiaoyu; Segars, W. Paul; Dixon, Robert L.; Samei, Ehsan

    2016-05-01

    Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460–7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18–70 years, weight range: 60–180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients ({{h}\\text{Organ}} ) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} with the organ dose coefficients ({{h}\\text{Organ}} ). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled

  17. Convolution-based estimation of organ dose in tube current modulated CT

    NASA Astrophysics Data System (ADS)

    Tian, Xiaoyu; Segars, W. Paul; Dixon, Robert L.; Samei, Ehsan

    2016-05-01

    Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460-7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18-70 years, weight range: 60-180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients ({{h}\\text{Organ}} ) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} with the organ dose coefficients ({{h}\\text{Organ}} ). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled. The

  18. Lexan Linear Shaped Charge Holder with Magnets and Backing Plate

    NASA Technical Reports Server (NTRS)

    Maples, Matthew W.; Dutton, Maureen L.; Hacker, Scott C.; Dean, Richard J.; Kidd, Nicholas; Long, Chris; Hicks, Robert C.

    2013-01-01

    A method was developed for cutting a fabric structural member in an inflatable module, without damaging the internal structure of the module, using linear shaped charge. Lexan and magnets are used in a charge holder to precisely position the linear shaped charge over the desired cut area. Two types of charge holders have been designed, each with its own backing plate. One holder cuts fabric straps in the vertical configuration, and the other charge holder cuts fabric straps in the horizontal configuration.

  19. Shape-Morphing Nanocomposite Origami

    PubMed Central

    2015-01-01

    Nature provides a vast array of solid materials that repeatedly and reversibly transform in shape in response to environmental variations. This property is essential, for example, for new energy-saving technologies, efficient collection of solar radiation, and thermal management. Here we report a similar shape-morphing mechanism using differential swelling of hydrophilic polyelectrolyte multilayer inkjets deposited on an LBL carbon nanotube (CNT) composite. The out-of-plane deflection can be precisely controlled, as predicted by theoretical analysis. We also demonstrate a controlled and stimuli-responsive twisting motion on a spiral-shaped LBL nanocomposite. By mimicking the motions achieved in nature, this method offers new opportunities for the design and fabrication of functional stimuli-responsive shape-morphing nanoscale and microscale structures for a variety of applications. PMID:24689908

  20. NCSX Vacuum Vessel Fabrication

    SciTech Connect

    Viola, M. E.; Brown, T.; Heitzenroeder, P.; Malinowski, F.; Reiersen, W.; Sutton, L.; Goranson, P.; Nelson, B.; Cole, M.; Manuel, M.; McCorkle, D.

    2005-10-07

    The National Compact Stellarator Experiment (NCSX) is being constructed at the Princeton Plasma Physics Laboratory (PPPL) in conjunction with the Oak Ridge National Laboratory (ORNL). The goal of this experiment is to develop a device which has the steady state properties of a traditional stellarator along with the high performance characteristics of a tokamak. A key element of this device is its highly shaped Inconel 625 vacuum vessel. This paper describes the manufacturing of the vessel. The vessel is being fabricated by Major Tool and Machine, Inc. (MTM) in three identical 120º vessel segments, corresponding to the three NCSX field periods, in order to accommodate assembly of the device. The port extensions are welded on, leak checked, cut off within 1" of the vessel surface at MTM and then reattached at PPPL, to accommodate assembly of the close-fitting modular coils that surround the vessel. The 120º vessel segments are formed by welding two 60º segments together. Each 60º segment is fabricated by welding ten press-formed panels together over a collapsible welding fixture which is needed to precisely position the panels. The vessel is joined at assembly by welding via custom machined 8" (20.3 cm) wide spacer "spool pieces." The vessel must have a total leak rate less than 5 X 10-6 t-l/s, magnetic permeability less than 1.02μ, and its contours must be within 0.188" (4.76 mm). It is scheduled for completion in January 2006.

  1. Shape Determination for Deformed Cavities

    SciTech Connect

    Lee, Lie-Quan; Akcelik, Volkan; Chen, Sheng; Ge, Lixin; Li, Zenghai; Ng, Cho; Xiao, Liling; Ko, Kwok; Ghattas, Omar; /Texas U.

    2006-10-04

    A realistic superconducting RF cavity has its shape deformed comparing to its designed shape due to the loose tolerance in the fabrication process and the frequency tuning for its accelerating mode. A PDE-constrained optimization problem is proposed to determine the deformation of the cavity. A reduce space method is used to solve the PDE-constrained optimization problem where design sensitivities were computed using a continuous adjoint approach. A proof-of-concept example is given in which the deformation parameters of a single cavity-cell with two different types of deformation were computed.

  2. A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations.

    PubMed

    Roth, Holger R; Lu, Le; Seff, Ari; Cherry, Kevin M; Hoffman, Joanne; Wang, Shijun; Liu, Jiamin; Turkbey, Evrim; Summers, Ronald M

    2014-01-01

    Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards -100% sensitivity at the cost of high FP levels (-40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work.

  3. Free form fabrication of thermoplastic composites

    SciTech Connect

    Kaufman, S.G.; Spletzer, B.L.; Guess, T.R.

    1998-02-01

    This report describes the results of composites fabrication research sponsored by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories. They have developed, prototyped, and demonstrated the feasibility of a novel robotic technique for rapid fabrication of composite structures. Its chief innovation is that, unlike all other available fabrication methods, it does not require a mold. Instead, the structure is built patch by patch, using a rapidly reconfigurable forming surface, and a robot to position the evolving part. Both of these components are programmable, so only the control software needs to be changed to produce a new shape. Hence it should be possible to automatically program the system to produce a shape directly from an electronic model of it. It is therefore likely that the method will enable faster and less expensive fabrication of composites.

  4. A nonlinear convolution model for the evasion of CO2 injected into the deep ocean

    NASA Astrophysics Data System (ADS)

    Kheshgi, Haroon S.; Archer, David E.

    2004-02-01

    Deep ocean storage of CO2 captured from, for example, flue gases is being considered as a potential response option to global warming concerns. For storage to be effective, CO2 injected into the deep ocean must remain sequestered from the atmosphere for a long time. However, a fraction of CO2 injected into the deep ocean is expected to eventually evade into the atmosphere. This fraction is expected to depend on the time since injection, the location of injection, and the future atmospheric concentration of CO2. We approximate the evasion of injected CO2 at specific locations using a nonlinear convolution model including explicitly the nonlinear response of CO2 solubility to future CO2 concentration and alkalinity and Green's functions for the transport of CO2 from injection locations to the ocean surface as well as alkalinity response to seafloor CaCO3 dissolution. Green's functions are calculated from the results of a three-dimensional model for ocean carbon cycle for impulses of CO2 either released to the atmosphere or injected a locations deep in the Pacific and Atlantic oceans. CO2 transport in the three-dimensional (3-D) model is governed by offline tracer transport in the ocean interior, exchange of CO2 with the atmosphere, and dissolution of ocean sediments. The convolution model is found to accurately approximate results of the 3-D model in test cases including both deep-ocean injection and sediment dissolution. The convolution model allows comparison of the CO2 evasion delay achieved by deep ocean injection with notional scenarios for CO2 stabilization and the time extent of the fossil fuel era.

  5. De-convoluting mixed crude oil in Prudhoe Bay Field, North Slope, Alaska

    USGS Publications Warehouse

    Peters, K.E.; Scott, Ramos L.; Zumberge, J.E.; Valin, Z.C.; Bird, K.J.

    2008-01-01

    Seventy-four crude oil samples from the Barrow arch on the North Slope of Alaska were studied to assess the relative volumetric contributions from different source rocks to the giant Prudhoe Bay Field. We applied alternating least squares to concentration data (ALS-C) for 46 biomarkers in the range C19-C35 to de-convolute mixtures of oil generated from carbonate rich Triassic Shublik Formation and clay rich Jurassic Kingak Shale and Cretaceous Hue Shale-gamma ray zone (Hue-GRZ) source rocks. ALS-C results for 23 oil samples from the prolific Ivishak Formation reservoir of the Prudhoe Bay Field indicate approximately equal contributions from Shublik Formation and Hue-GRZ source rocks (37% each), less from the Kingak Shale (26%), and little or no contribution from other source rocks. These results differ from published interpretations that most oil in the Prudhoe Bay Field originated from the Shublik Formation source rock. With few exceptions, the relative contribution of oil from the Shublik Formation decreases, while that from the Hue-GRZ increases in reservoirs along the Barrow arch from Point Barrow in the northwest to Point Thomson in the southeast (???250 miles or 400 km). The Shublik contribution also decreases to a lesser degree between fault blocks within the Ivishak pool from west to east across the Prudhoe Bay Field. ALS-C provides a robust means to calculate the relative amounts of two or more oil types in a mixture. Furthermore, ALS-C does not require that pure end member oils be identified prior to analysis or that laboratory mixtures of these oils be prepared to evaluate mixing. ALS-C of biomarkers reliably de-convolutes mixtures because the concentrations of compounds in mixtures vary as linear functions of the amount of each oil type. ALS of biomarker ratios (ALS-R) cannot be used to de-convolute mixtures because compound ratios vary as nonlinear functions of the amount of each oil type.

  6. Convolutions of Rayleigh functions and their application to semi-linear equations in circular domains

    NASA Astrophysics Data System (ADS)

    Varlamov, Vladimir

    2007-03-01

    Rayleigh functions [sigma]l([nu]) are defined as series in inverse powers of the Bessel function zeros [lambda][nu],n[not equal to]0, where ; [nu] is the index of the Bessel function J[nu](x) and n=1,2,... is the number of the zeros. Convolutions of Rayleigh functions with respect to the Bessel index, are needed for constructing global-in-time solutions of semi-linear evolution equations in circular domains [V. Varlamov, On the spatially two-dimensional Boussinesq equation in a circular domain, Nonlinear Anal. 46 (2001) 699-725; V. Varlamov, Convolution of Rayleigh functions with respect to the Bessel index, J. Math. Anal. Appl. 306 (2005) 413-424]. The study of this new family of special functions was initiated in [V. Varlamov, Convolution of Rayleigh functions with respect to the Bessel index, J. Math. Anal. Appl. 306 (2005) 413-424], where the properties of R1(m) were investigated. In the present work a general representation of Rl(m) in terms of [sigma]l([nu]) is deduced. On the basis of this a representation for the function R2(m) is obtained in terms of the [psi]-function. An asymptotic expansion is computed for R2(m) as m-->[infinity]. Such asymptotics are needed for establishing function spaces for solutions of semi-linear equations in bounded domains with periodicity conditions in one coordinate. As an example of application of Rl(m) a forced Boussinesq equationutt-2b[Delta]ut=-[alpha][Delta]2u+[Delta]u+[beta][Delta](u2)+f with [alpha],b=const>0 and [beta]=const[set membership, variant]R is considered in a unit disc with homogeneous boundary and initial data. Construction of its global-in-time solutions involves the use of the functions R1(m) and R2(m) which are responsible for the nonlinear smoothing effect.

  7. Study of the Auger line shape of polyethylene and diamond

    NASA Technical Reports Server (NTRS)

    Dayan, M.; Pepper, S. V.

    1984-01-01

    The KVV Auger electron line shapes of carbon in polyethylene and diamond have been studied. The spectra were obtained in derivative form by electron beam excitation. They were treated by background subtraction, integration and deconvolution to produce the intrinsic Auger line shape. Electron energy loss spectra provided the response function in the deconvolution procedure. The line shape from polyethylene is compared with spectra from linear alkanes and with a previous spectrum of Kelber et al. Both spectra are compared with the self-convolution of their full valence band densities of states and of their p-projected densities. The experimental spectra could not be understood in terms of existing theories. This is so even when correlation effects are qualitatively taken into account account to the theories of Cini and Sawatzky and Lenselink.

  8. Automatic breast density classification using a convolutional neural network architecture search procedure

    NASA Astrophysics Data System (ADS)

    Fonseca, Pablo; Mendoza, Julio; Wainer, Jacques; Ferrer, Jose; Pinto, Joseph; Guerrero, Jorge; Castaneda, Benjamin

    2015-03-01

    Breast parenchymal density is considered a strong indicator of breast cancer risk and therefore useful for preventive tasks. Measurement of breast density is often qualitative and requires the subjective judgment of radiologists. Here we explore an automatic breast composition classification workflow based on convolutional neural networks for feature extraction in combination with a support vector machines classifier. This is compared to the assessments of seven experienced radiologists. The experiments yielded an average kappa value of 0.58 when using the mode of the radiologists' classifications as ground truth. Individual radiologist performance against this ground truth yielded kappa values between 0.56 and 0.79.

  9. Using convolutional neural networks for human activity classification on micro-Doppler radar spectrograms

    NASA Astrophysics Data System (ADS)

    Jordan, Tyler S.

    2016-05-01

    This paper presents the findings of using convolutional neural networks (CNNs) to classify human activity from micro-Doppler features. An emphasis on activities involving potential security threats such as holding a gun are explored. An automotive 24 GHz radar on chip was used to collect the data and a CNN (normally applied to image classification) was trained on the resulting spectrograms. The CNN achieves an error rate of 1.65 % on classifying running vs. walking, 17.3 % error on armed walking vs. unarmed walking, and 22 % on classifying six different actions.

  10. Robust and accurate transient light transport decomposition via convolutional sparse coding.

    PubMed

    Hu, Xuemei; Deng, Yue; Lin, Xing; Suo, Jinli; Dai, Qionghai; Barsi, Christopher; Raskar, Ramesh

    2014-06-01

    Ultrafast sources and detectors have been used to record the time-resolved scattering of light propagating through macroscopic scenes. In the context of computational imaging, decomposition of this transient light transport (TLT) is useful for applications, such as characterizing materials, imaging through diffuser layers, and relighting scenes dynamically. Here, we demonstrate a method of convolutional sparse coding to decompose TLT into direct reflections, inter-reflections, and subsurface scattering. The method relies on the sparsity composition of the time-resolved kernel. We show that it is robust and accurate to noise during the acquisition process.

  11. Diffuse dispersive delay and the time convolution/attenuation of transients

    NASA Technical Reports Server (NTRS)

    Bittner, Burt J.

    1991-01-01

    Test data and analytic evaluations are presented to show that relatively poor 100 KHz shielding of 12 Db can effectively provide an electromagnetic pulse transient reduction of 100 Db. More importantly, several techniques are shown for lightning surge attenuation as an alternative to crowbar, spark gap, or power zener type clipping which simply reflects the surge. A time delay test method is shown which allows CW testing, along with a convolution program to define transient shielding effectivity where the Fourier phase characteristics of the transient are known or can be broadly estimated.

  12. Cygrid: Cython-powered convolution-based gridding module for Python

    NASA Astrophysics Data System (ADS)

    Winkel, B.; Lenz, D.; Flöer, L.

    2016-06-01

    The Python module Cygrid grids (resamples) data to any collection of spherical target coordinates, although its typical application involves FITS maps or data cubes. The module supports the FITS world coordinate system (WCS) standard; its underlying algorithm is based on the convolution of the original samples with a 2D Gaussian kernel. A lookup table scheme allows parallelization of the code and is combined with the HEALPix tessellation of the sphere for fast neighbor searches. Cygrid's runtime scales between O(n) and O(nlog n), with n being the number of input samples.

  13. A convolutional recursive modified Self Organizing Map for handwritten digits recognition.

    PubMed

    Mohebi, Ehsan; Bagirov, Adil

    2014-12-01

    It is well known that the handwritten digits recognition is a challenging problem. Different classification algorithms have been applied to solve it. Among them, the Self Organizing Maps (SOM) produced promising results. In this paper, first we introduce a Modified SOM for the vector quantization problem with improved initialization process and topology preservation. Then we develop a Convolutional Recursive Modified SOM and apply it to the problem of handwritten digits recognition. The computational results obtained using the well known MNIST dataset demonstrate the superiority of the proposed algorithm over the existing SOM-based algorithms.

  14. Parity retransmission hybrid ARQ using rate 1/2 convolutional codes on a nonstationary channel

    NASA Technical Reports Server (NTRS)

    Lugand, Laurent R.; Costello, Daniel J., Jr.; Deng, Robert H.

    1989-01-01

    A parity retransmission hybrid automatic repeat request (ARQ) scheme is proposed which uses rate 1/2 convolutional codes and Viterbi decoding. A protocol is described which is capable of achieving higher throughputs than previously proposed parity retransmission schemes. The performance analysis is based on a two-state Markov model of a nonstationary channel. This model constitutes a first approximation to a nonstationary channel. The two-state channel model is used to analyze the throughput and undetected error probability of the protocol presented when the receiver has both an infinite and a finite buffer size. It is shown that the throughput improves as the channel becomes more bursty.

  15. Processing circuit with asymmetry corrector and convolutional encoder for digital data

    NASA Technical Reports Server (NTRS)

    Pfiffner, Harold J. (Inventor)

    1987-01-01

    A processing circuit is provided for correcting for input parameter variations, such as data and clock signal symmetry, phase offset and jitter, noise and signal amplitude, in incoming data signals. An asymmetry corrector circuit performs the correcting function and furnishes the corrected data signals to a convolutional encoder circuit. The corrector circuit further forms a regenerated clock signal from clock pulses in the incoming data signals and another clock signal at a multiple of the incoming clock signal. These clock signals are furnished to the encoder circuit so that encoded data may be furnished to a modulator at a high data rate for transmission.

  16. Vibration analysis of FG cylindrical shells with power-law index using discrete singular convolution technique

    NASA Astrophysics Data System (ADS)

    Mercan, Kadir; Demir, Çiǧdem; Civalek, Ömer

    2016-01-01

    In the present manuscript, free vibration response of circular cylindrical shells with functionally graded material (FGM) is investigated. The method of discrete singular convolution (DSC) is used for numerical solution of the related governing equation of motion of FGM cylindrical shell. The constitutive relations are based on the Love's first approximation shell theory. The material properties are graded in the thickness direction according to a volume fraction power law indexes. Frequency values are calculated for different types of boundary conditions, material and geometric parameters. In general, close agreement between the obtained results and those of other researchers has been found.

  17. Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding

    PubMed Central

    Johnson, Rie; Zhang, Tong

    2016-01-01

    This paper presents a new semi-supervised framework with convolutional neural networks (CNNs) for text categorization. Unlike the previous approaches that rely on word embeddings, our method learns embeddings of small text regions from unlabeled data for integration into a supervised CNN. The proposed scheme for embedding learning is based on the idea of two-view semi-supervised learning, which is intended to be useful for the task of interest even though the training is done on unlabeled data. Our models achieve better results than previous approaches on sentiment classification and topic classification tasks. PMID:27087766

  18. Performance of DPSK with convolutional encoding on time-varying fading channels

    NASA Technical Reports Server (NTRS)

    Mui, S. Y.; Modestino, J. W.

    1977-01-01

    The bit error probability performance of a differentially-coherent phase-shift keyed (DPSK) modem with convolutional encoding and Viterbi decoding on time-varying fading channels is examined. Both the Rician and the lognormal channels are considered. Bit error probability upper bounds on fully-interleaved (zero-memory) fading channels are derived and substantiated by computer simulation. It is shown that the resulting coded system performance is a relatively insensitive function of the choice of channel model provided that the channel parameters are related according to the correspondence developed as part of this paper. Finally, a comparison of DPSK with a number of other modulation strategies is provided.

  19. Effects of magnetic field on the shape memory behavior of single and polycrystalline magnetic shape memory alloys

    NASA Astrophysics Data System (ADS)

    Turabi, Ali Sadi

    Shape memory alloys and polymers have been extensively researched recently because of their unique ability to recover large deformations. Shape memory polymers (SMPs) are able to recover large deformations compared to shape memory alloys (SMAs), although SMAs have higher strength and are able to generate more stress during recovery. This project focuses on procedure for fabrication and Finite Element Modeling (FEM) of a shape memory composite actuator. First, SMP was characterized to reveal its mechanical properties. Specifically, glass transition temperature, the effects of temperature and strain rate on compressive response and recovery properties of shape memory polymer were studied. Then, shape memory properties of a NiTi wire, including transformation temperatures and stress generation, were investigated. SMC actuator was fabricated by using epoxy based SMP and NiTi SMA wire. Experimental tests confirmed the reversible behavior of fabricated shape memory composites. (Abstract shortened by ProQuest.).

  20. Equilibrium Shaping

    NASA Astrophysics Data System (ADS)

    Izzo, Dario; Petazzi, Lorenzo

    2006-08-01

    We present a satellite path planning technique able to make identical spacecraft aquire a given configuration. The technique exploits a behaviour-based approach to achieve an autonomous and distributed control over the relative geometry making use of limited sensorial information. A desired velocity is defined for each satellite as a sum of different contributions coming from generic high level behaviours: forcing the final desired configuration the behaviours are further defined by an inverse dynamic calculation dubbed Equilibrium Shaping. We show how considering only three different kind of behaviours it is possible to acquire a number of interesting formations and we set down the theoretical framework to find the entire set. We find that allowing a limited amount of communication the technique may be used also to form complex lattice structures. Several control feedbacks able to track the desired velocities are introduced and discussed. Our results suggest that sliding mode control is particularly appropriate in connection with the developed technique.

  1. Concatenated coding systems employing a unit-memory convolutional code and a byte-oriented decoding algorithm

    NASA Technical Reports Server (NTRS)

    Lee, L. N.

    1976-01-01

    Concatenated coding systems utilizing a convolutional code as the inner code and a Reed-Solomon code as the outer code are considered. In order to obtain very reliable communications over a very noisy channel with relatively small coding complexity, it is proposed to concatenate a byte oriented unit memory convolutional code with an RS outer code whose symbol size is one byte. It is further proposed to utilize a real time minimal byte error probability decoding algorithm, together with feedback from the outer decoder, in the decoder for the inner convolutional code. The performance of the proposed concatenated coding system is studied, and the improvement over conventional concatenated systems due to each additional feature is isolated.

  2. Concatenated coding systems employing a unit-memory convolutional code and a byte-oriented decoding algorithm

    NASA Technical Reports Server (NTRS)

    Lee, L.-N.

    1977-01-01

    Concatenated coding systems utilizing a convolutional code as the inner code and a Reed-Solomon code as the outer code are considered. In order to obtain very reliable communications over a very noisy channel with relatively modest coding complexity, it is proposed to concatenate a byte-oriented unit-memory convolutional code with an RS outer code whose symbol size is one byte. It is further proposed to utilize a real-time minimal-byte-error probability decoding algorithm, together with feedback from the outer decoder, in the decoder for the inner convolutional code. The performance of the proposed concatenated coding system is studied, and the improvement over conventional concatenated systems due to each additional feature is isolated.

  3. Fabricating customized hydrogel contact lens

    PubMed Central

    Childs, Andre; Li, Hao; Lewittes, Daniella M.; Dong, Biqin; Liu, Wenzhong; Shu, Xiao; Sun, Cheng; Zhang, Hao F.

    2016-01-01

    Contact lenses are increasingly used in laboratories for in vivo animal retinal imaging and pre-clinical studies. The lens shapes often need modification to optimally fit corneas of individual test subjects. However, the choices from commercially available contact lenses are rather limited. Here, we report a flexible method to fabricate customized hydrogel contact lenses. We showed that the fabricated hydrogel is highly transparent, with refractive indices ranging from 1.42 to 1.45 in the spectra range from 400 nm to 800 nm. The Young’s modulus (1.47 MPa) and hydrophobicity (with a sessile drop contact angle of 40.5°) have also been characterized experimentally. Retinal imaging using optical coherence tomography in rats wearing our customized contact lenses has the quality comparable to the control case without the contact lens. Our method could significantly reduce the cost and the lead time for fabricating soft contact lenses with customized shapes, and benefit the laboratorial-used contact lenses in pre-clinical studies. PMID:27748361

  4. Fabricating customized hydrogel contact lens

    NASA Astrophysics Data System (ADS)

    Childs, Andre; Li, Hao; Lewittes, Daniella M.; Dong, Biqin; Liu, Wenzhong; Shu, Xiao; Sun, Cheng; Zhang, Hao F.

    2016-10-01

    Contact lenses are increasingly used in laboratories for in vivo animal retinal imaging and pre-clinical studies. The lens shapes often need modification to optimally fit corneas of individual test subjects. However, the choices from commercially available contact lenses are rather limited. Here, we report a flexible method to fabricate customized hydrogel contact lenses. We showed that the fabricated hydrogel is highly transparent, with refractive indices ranging from 1.42 to 1.45 in the spectra range from 400 nm to 800 nm. The Young’s modulus (1.47 MPa) and hydrophobicity (with a sessile drop contact angle of 40.5°) have also been characterized experimentally. Retinal imaging using optical coherence tomography in rats wearing our customized contact lenses has the quality comparable to the control case without the contact lens. Our method could significantly reduce the cost and the lead time for fabricating soft contact lenses with customized shapes, and benefit the laboratorial-used contact lenses in pre-clinical studies.

  5. Assessing the Firing Properties of the Electrically Stimulated Auditory Nerve Using a Convolution Model.

    PubMed

    Strahl, Stefan B; Ramekers, Dyan; Nagelkerke, Marjolijn M B; Schwarz, Konrad E; Spitzer, Philipp; Klis, Sjaak F L; Grolman, Wilko; Versnel, Huib

    2016-01-01

    The electrically evoked compound action potential (eCAP) is a routinely performed measure of the auditory nerve in cochlear implant users. Using a convolution model of the eCAP, additional information about the neural firing properties can be obtained, which may provide relevant information about the health of the auditory nerve. In this study, guinea pigs with various degrees of nerve degeneration were used to directly relate firing properties to nerve histology. The same convolution model was applied on human eCAPs to examine similarities and ultimately to examine its clinical applicability. For most eCAPs, the estimated nerve firing probability was bimodal and could be parameterised by two Gaussian distributions with an average latency difference of 0.4 ms. The ratio of the scaling factors of the late and early component increased with neural degeneration in the guinea pig. This ratio decreased with stimulation intensity in humans. The latency of the early component decreased with neural degeneration in the guinea pig. Indirectly, this was observed in humans as well, assuming that the cochlear base exhibits more neural degeneration than the apex. Differences between guinea pigs and humans were observed, among other parameters, in the width of the early component: very robust in guinea pig, and dependent on stimulation intensity and cochlear region in humans. We conclude that the deconvolution of the eCAP is a valuable addition to existing analyses, in particular as it reveals two separate firing components in the auditory nerve. PMID:27080655

  6. Efficient pedestrian detection from aerial vehicles with object proposals and deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Minnehan, Breton; Savakis, Andreas

    2016-05-01

    As Unmanned Aerial Systems grow in numbers, pedestrian detection from aerial platforms is becoming a topic of increasing importance. By providing greater contextual information and a reduced potential for occlusion, the aerial vantage point provided by Unmanned Aerial Systems is highly advantageous for many surveillance applications, such as target detection, tracking, and action recognition. However, due to the greater distance between the camera and scene, targets of interest in aerial imagery are generally smaller and have less detail. Deep Convolutional Neural Networks (CNN's) have demonstrated excellent object classification performance and in this paper we adopt them to the problem of pedestrian detection from aerial platforms. We train a CNN with five layers consisting of three convolution-pooling layers and two fully connected layers. We also address the computational inefficiencies of the sliding window method for object detection. In the sliding window configuration, a very large number of candidate patches are generated from each frame, while only a small number of them contain pedestrians. We utilize the Edge Box object proposal generation method to screen candidate patches based on an "objectness" criterion, so that only regions that are likely to contain objects are processed. This method significantly reduces the number of image patches processed by the neural network and makes our classification method very efficient. The resulting two-stage system is a good candidate for real-time implementation onboard modern aerial vehicles. Furthermore, testing on three datasets confirmed that our system offers high detection accuracy for terrestrial pedestrian detection in aerial imagery.

  7. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

    PubMed

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-18

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation.

  8. The Probabilistic Convolution Tree: Efficient Exact Bayesian Inference for Faster LC-MS/MS Protein Inference

    PubMed Central

    Serang, Oliver

    2014-01-01

    Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called “causal independence”). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to and the space to where is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions. PMID:24626234

  9. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

    PubMed

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation. PMID:26797612

  10. Partial fourier reconstruction through data fitting and convolution in k-space.

    PubMed

    Huang, Feng; Lin, Wei; Li, Yu

    2009-11-01

    A partial Fourier acquisition scheme has been widely adopted for fast imaging. There are two problems associated with the existing techniques. First, the majority of the existing techniques demodulate the phase information and cannot provide improved phase information over zero-padding. Second, serious artifacts can be observed in reconstruction when the phase changes rapidly because the low-resolution phase estimate in the image space is prone to error. To tackle these two problems, a novel and robust method is introduced for partial Fourier reconstruction, using k-space convolution. In this method, the phase information is implicitly estimated in k-space through data fitting; the approximated phase information is applied to recover the unacquired k-space data through Hermitian operation and convolution in k-space. In both spin echo and gradient echo imaging experiments, the proposed method consistently produced images with the lowest error level when compared to Cuppen's algorithm, projection onto convex sets-based iterative algorithm, and Homodyne algorithm. Significant improvements are observed in images with rapid phase change. Besides the improvement on magnitude, the phase map of the images reconstructed by the proposed method also has significantly lower error level than conventional methods.

  11. Large patch convolutional neural networks for the scene classification of high spatial resolution imagery

    NASA Astrophysics Data System (ADS)

    Zhong, Yanfei; Fei, Feng; Zhang, Liangpei

    2016-04-01

    The increase of the spatial resolution of remote-sensing sensors helps to capture the abundant details related to the semantics of surface objects. However, it is difficult for the popular object-oriented classification approaches to acquire higher level semantics from the high spatial resolution remote-sensing (HSR-RS) images, which is often referred to as the "semantic gap." Instead of designing sophisticated operators, convolutional neural networks (CNNs), a typical deep learning method, can automatically discover intrinsic feature descriptors from a large number of input images to bridge the semantic gap. Due to the small data volume of the available HSR-RS scene datasets, which is far away from that of the natural scene datasets, there have been few reports of CNN approaches for HSR-RS image scene classifications. We propose a practical CNN architecture for HSR-RS scene classification, named the large patch convolutional neural network (LPCNN). The large patch sampling is used to generate hundreds of possible scene patches for the feature learning, and a global average pooling layer is used to replace the fully connected network as the classifier, which can greatly reduce the total parameters. The experiments confirm that the proposed LPCNN can learn effective local features to form an effective representation for different land-use scenes, and can achieve a performance that is comparable to the state-of-the-art on public HSR-RS scene datasets.

  12. Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

    PubMed Central

    Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

    2016-01-01

    We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods. PMID:26864172

  13. Assessing the Firing Properties of the Electrically Stimulated Auditory Nerve Using a Convolution Model.

    PubMed

    Strahl, Stefan B; Ramekers, Dyan; Nagelkerke, Marjolijn M B; Schwarz, Konrad E; Spitzer, Philipp; Klis, Sjaak F L; Grolman, Wilko; Versnel, Huib

    2016-01-01

    The electrically evoked compound action potential (eCAP) is a routinely performed measure of the auditory nerve in cochlear implant users. Using a convolution model of the eCAP, additional information about the neural firing properties can be obtained, which may provide relevant information about the health of the auditory nerve. In this study, guinea pigs with various degrees of nerve degeneration were used to directly relate firing properties to nerve histology. The same convolution model was applied on human eCAPs to examine similarities and ultimately to examine its clinical applicability. For most eCAPs, the estimated nerve firing probability was bimodal and could be parameterised by two Gaussian distributions with an average latency difference of 0.4 ms. The ratio of the scaling factors of the late and early component increased with neural degeneration in the guinea pig. This ratio decreased with stimulation intensity in humans. The latency of the early component decreased with neural degeneration in the guinea pig. Indirectly, this was observed in humans as well, assuming that the cochlear base exhibits more neural degeneration than the apex. Differences between guinea pigs and humans were observed, among other parameters, in the width of the early component: very robust in guinea pig, and dependent on stimulation intensity and cochlear region in humans. We conclude that the deconvolution of the eCAP is a valuable addition to existing analyses, in particular as it reveals two separate firing components in the auditory nerve.

  14. Accelerating protein docking in ZDOCK using an advanced 3D convolution library.

    PubMed

    Pierce, Brian G; Hourai, Yuichiro; Weng, Zhiping

    2011-01-01

    Computational prediction of the 3D structures of molecular interactions is a challenging area, often requiring significant computational resources to produce structural predictions with atomic-level accuracy. This can be particularly burdensome when modeling large sets of interactions, macromolecular assemblies, or interactions between flexible proteins. We previously developed a protein docking program, ZDOCK, which uses a fast Fourier transform to perform a 3D search of the spatial degrees of freedom between two molecules. By utilizing a pairwise statistical potential in the ZDOCK scoring function, there were notable gains in docking accuracy over previous versions, but this improvement in accuracy came at a substantial computational cost. In this study, we incorporated a recently developed 3D convolution library into ZDOCK, and additionally modified ZDOCK to dynamically orient the input proteins for more efficient convolution. These modifications resulted in an average of over 8.5-fold improvement in running time when tested on 176 cases in a newly released protein docking benchmark, as well as substantially less memory usage, with no loss in docking accuracy. We also applied these improvements to a previous version of ZDOCK that uses a simpler non-pairwise atomic potential, yielding an average speed improvement of over 5-fold on the docking benchmark, while maintaining predictive success. This permits the utilization of ZDOCK for more intensive tasks such as docking flexible molecules and modeling of interactomes, and can be run more readily by those with limited computational resources. PMID:21949741

  15. Noise-induced bias for convolution-based interpolation in digital image correlation.

    PubMed

    Su, Yong; Zhang, Qingchuan; Gao, Zeren; Xu, Xiaohai

    2016-01-25

    In digital image correlation (DIC), the noise-induced bias is significant if the noise level is high or the contrast of the image is low. However, existing methods for the estimation of the noise-induced bias are merely applicable to traditional interpolation methods such as linear and cubic interpolation, but are not applicable to generalized interpolation methods such as BSpline and OMOMS. Both traditional interpolation and generalized interpolation belong to convolution-based interpolation. Considering the widely use of generalized interpolation, this paper presents a theoretical analysis of noise-induced bias for convolution-based interpolation. A sinusoidal approximate formula for noise-induced bias is derived; this formula motivates an estimating strategy which is with speed, ease, and accuracy; furthermore, based on this formula, the mechanism of sophisticated interpolation methods generally reducing noise-induced bias is revealed. The validity of the theoretical analysis is established by both numerical simulations and actual subpixel translation experiment. Compared to existing methods, formulae provided by this paper are simpler, briefer, and more general. In addition, a more intuitionistic explanation of the cause of noise-induced bias is provided by quantitatively characterized the position-dependence of noise variability in the spatial domain.

  16. Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier

    NASA Astrophysics Data System (ADS)

    Rocco, Dominick Rosario

    The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of $[special characters omitted]. and theta23 and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74 x 1020 POT, 14 kt equivalent) to provide new best-fit measurements of sin2(theta23) = 0.43 (with a statistically-degenerate compliment near 0.60) and [special characters omitted]..

  17. Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.

    PubMed

    Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

    2016-01-01

    We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.

  18. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation.

    PubMed

    Girshick, Ross; Donahue, Jeff; Darrell, Trevor; Malik, Jitendra

    2016-01-01

    Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent. Our approach combines two ideas: (1) one can apply high-capacity convolutional networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R-CNN or Region-based Convolutional Network. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.

  19. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    PubMed Central

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

  20. Non-spherical particle generation from 4D optofluidic fabrication.

    PubMed

    Paulsen, Kevin S; Chung, Aram J

    2016-08-01

    Particles with non-spherical shapes can exhibit properties which are not available from spherical shaped particles. Complex shaped particles can provide unique benefits for areas such as drug delivery, tissue engineering, structural materials, and self-assembly building blocks. Current methods of creating complex shaped particles such as 3D printing, photolithography, and imprint lithography are limited by either slow speeds, shape limitations, or expensive processes. Previously, we presented a novel microfluidic flow lithography fabrication scheme combined with fluid inertia called optofluidic fabrication for the creation of complex shaped three-dimensional (3D) particles. This process was able to address the aforementioned limits and overcome two-dimensional shape limitations faced by traditional flow lithography methods; however, all of the created 3D particle shapes displayed top-down symmetry. Here, by introducing the time dimension into our existing optofluidic fabrication process, we break this top-down symmetry, generating fully asymmetric 3D particles where we termed the process: four-dimensional (4D) optofluidic fabrication. This 4D optofluidic fabrication is comprised of three sequential procedures. First, density mismatched precursor fluids flow past pillars within fluidic channels to manipulate the flow cross sections via fluid inertia. Next, the time dimension is incorporated by stopping the flow and allowing the denser fluids to settle by gravity to create asymmetric flow cross sections. Finally, the fluids are exposed to patterned ultraviolet (UV) light in order to polymerize fully asymmetric 3D-shaped particles. By varying inertial flow shaping, gravity-induced flow shaping, and UV light patterns, 4D optofluidic fabrication can create an infinite set of complex shaped asymmetric particles. PMID:27092661

  1. Non-spherical particle generation from 4D optofluidic fabrication.

    PubMed

    Paulsen, Kevin S; Chung, Aram J

    2016-08-01

    Particles with non-spherical shapes can exhibit properties which are not available from spherical shaped particles. Complex shaped particles can provide unique benefits for areas such as drug delivery, tissue engineering, structural materials, and self-assembly building blocks. Current methods of creating complex shaped particles such as 3D printing, photolithography, and imprint lithography are limited by either slow speeds, shape limitations, or expensive processes. Previously, we presented a novel microfluidic flow lithography fabrication scheme combined with fluid inertia called optofluidic fabrication for the creation of complex shaped three-dimensional (3D) particles. This process was able to address the aforementioned limits and overcome two-dimensional shape limitations faced by traditional flow lithography methods; however, all of the created 3D particle shapes displayed top-down symmetry. Here, by introducing the time dimension into our existing optofluidic fabrication process, we break this top-down symmetry, generating fully asymmetric 3D particles where we termed the process: four-dimensional (4D) optofluidic fabrication. This 4D optofluidic fabrication is comprised of three sequential procedures. First, density mismatched precursor fluids flow past pillars within fluidic channels to manipulate the flow cross sections via fluid inertia. Next, the time dimension is incorporated by stopping the flow and allowing the denser fluids to settle by gravity to create asymmetric flow cross sections. Finally, the fluids are exposed to patterned ultraviolet (UV) light in order to polymerize fully asymmetric 3D-shaped particles. By varying inertial flow shaping, gravity-induced flow shaping, and UV light patterns, 4D optofluidic fabrication can create an infinite set of complex shaped asymmetric particles.

  2. Dip-molded t-shaped cannula

    NASA Technical Reports Server (NTRS)

    Broyles, H. F.; Cuddihy, E. F.; Moacanin, J.

    1978-01-01

    Cannula, fabricated out of polyetherurethane, has been designed for long-term service. Improved cannula is T-shaped to collect blood from both directions, thus replacing two conventional cannulas that are usually required and eliminating need for large surgical wound. It is fabricated by using dip-molding process that can be adapted to other elastomeric objects having complex shapes. Dimensions of cannula were chosen to optimize its blood-flow properties and to reduce danger of excessive clotting, making it suitable for continuous service up to 21 days in vein or artery of patient.

  3. Polymorphous computing fabric

    DOEpatents

    Wolinski, Christophe Czeslaw; Gokhale, Maya B.; McCabe, Kevin Peter

    2011-01-18

    Fabric-based computing systems and methods are disclosed. A fabric-based computing system can include a polymorphous computing fabric that can be customized on a per application basis and a host processor in communication with said polymorphous computing fabric. The polymorphous computing fabric includes a cellular architecture that can be highly parameterized to enable a customized synthesis of fabric instances for a variety of enhanced application performances thereof. A global memory concept can also be included that provides the host processor random access to all variables and instructions associated with the polymorphous computing fabric.

  4. On the application of a fast polynomial transform and the Chinese remainder theorem to compute a two-dimensional convolution

    NASA Technical Reports Server (NTRS)

    Truong, T. K.; Lipes, R.; Reed, I. S.; Wu, C.

    1980-01-01

    A fast algorithm is developed to compute two dimensional convolutions of an array of d sub 1 X d sub 2 complex number points, where d sub 2 = 2(M) and d sub 1 = 2(m-r+) for some 1 or = r or = m. This algorithm requires fewer multiplications and about the same number of additions as the conventional fast fourier transform method for computing the two dimensional convolution. It also has the advantage that the operation of transposing the matrix of data can be avoided.

  5. Chemically enabled nanostructure fabrication

    NASA Astrophysics Data System (ADS)

    Huo, Fengwei

    The first part of the dissertation explored ways of chemically synthesizing new nanoparticles and biologically guided assembly of nanoparticle building blocks. Chapter two focuses on synthesizing three-layer composite magnetic nanoparticles with a gold shell which can be easily functionalized with other biomolecules. The three-layer magnetic nanoparticles, when functionalized with oligonucleotides, exhibit the surface chemistry, optical properties, and cooperative DNA binding properties of gold nanoparticle probes, while maintaining the magnetic properties of the Fe3O4 inner shell. Chapter three describes a new method for synthesizing nanoparticles asymmetrically functionalized with oligonucleotides and the use of these novel building blocks to create satellite structures. This synthetic capability allows one to introduce valency into such structures and then use that valency to direct particle assembly events. The second part of the thesis explored approaches of nanostructure fabrication on substrates. Chapter four focuses on the development of a new scanning probe contact printing method, polymer pen lithography (PPL), which combines the advantages of muCp and DPN to achieve high-throughput, flexible molecular printing. PPL uses a soft elastomeric tip array, rather than tips mounted on individual cantilevers, to deliver inks to a surface in a "direct write" manner. Arrays with as many as ˜11 million pyramid-shaped pens can be brought into contact with substrates and readily leveled optically in order to insure uniform pattern development. Chapter five describes gel pen lithography, which uses a gel to fabricate pen array. Gel pen lithography is a low-cost, high-throughput nanolithography method especially useful for biomaterials patterning and aqueous solution patterning which makes it a supplement to DPN and PPL. Chapter 6 shows a novel form of optical nanolithography, Beam Pen Lithography (BPL), which uses an array of NSOM pens to do nanoscale optical

  6. Fabrication of zein nanostructure

    NASA Astrophysics Data System (ADS)

    Luecha, Jarupat

    resins. The soft lithography technique was mainly used to fabricate micro and nanostructures on zein films. Zein material well-replicated small structures with the smallest size at sub micrometer scale that resulted in interesting photonic properties. The bonding method was also developed for assembling portable zein microfluidic devices with small shape distortion. Zein-zein and zein-glass microfluidic devices demonstrated sufficient strength to facilitate fluid flow in a complex microfluidic design with no leakage. Aside from the fabrication technique development, several potential applications of this environmentally friendly microfluidic device were investigated. The concentration gradient manipulation of Rhodamine B solution in zein-glass microfluidic devices was demonstrated. The diffusion of small molecules such as fluorescent dye into the wall of the zein microfluidic channels was observed. However, with this formulation, zein microfluidic devices were not suitable for cell culture applications. This pioneer study covered a wide spectrum of the implementation of the two nanotechnology approaches to advance zein biomaterial which provided proof of fundamental concepts as well as presenting some limitations. The findings in this study can lead to several innovative research opportunities of advanced zein biomaterials with broad applications. The information from the study of zein nanocomposite structure allows the packaging industry to develop the low cost biodegradable materials with physical property improvement. The information from the study of the zein microfluidic devices allows agro-industry to develop the nanotechnology-enabled microfluidic sensors fabricated entirely from biodegradable polymer for on-site disease or contaminant detection in the fields of food and agriculture.

  7. Superordinate Shape Classification Using Natural Shape Statistics

    ERIC Educational Resources Information Center

    Wilder, John; Feldman, Jacob; Singh, Manish

    2011-01-01

    This paper investigates the classification of shapes into broad natural categories such as "animal" or "leaf". We asked whether such coarse classifications can be achieved by a simple statistical classification of the shape skeleton. We surveyed databases of natural shapes, extracting shape skeletons and tabulating their parameters within each…

  8. Material cutting, shaping, and forming: A compilation

    NASA Technical Reports Server (NTRS)

    1974-01-01

    Information is presented concerning cutting, shaping, and forming of materials, and the equipment and techniques required for utilizing these materials. The use of molds, electrical fields, and mechanical devices are related to forming materials. Material cutting methods by devices including borers and slicers are presented along with chemical techniques. Shaping and fabrication techniques are described for tubing, honeycomb panels, and ceramic structures. The characteristics of the materials are described. Patent information is included.

  9. Shape Determination for Deformed Electromagnetic Cavities

    SciTech Connect

    Akcelik, Volkan; Ko, Kwok; Lee, Lie-Quan; Li, Zhenghai; Ng, Cho-Kuen; Xiao, Liling; /SLAC

    2007-12-10

    The measured physical parameters of a superconducting cavity differ from those of the designed ideal cavity. This is due to shape deviations caused by both loose machine tolerances during fabrication and by the tuning process for the accelerating mode. We present a shape determination algorithm to solve for the unknown deviations from the ideal cavity using experimentally measured cavity data. The objective is to match the results of the deformed cavity model to experimental data through least-squares minimization. The inversion variables are unknown shape deformation parameters that describe perturbations of the ideal cavity. The constraint is the Maxwell eigenvalue problem. We solve the nonlinear optimization problem using a line-search based reduced space Gauss-Newton method where we compute shape sensitivities with a discrete adjoint approach. We present two shape determination examples, one from synthetic and the other from experimental data. The results demonstrate that the proposed algorithm is very effective in determining the deformed cavity shape.

  10. Simplified Fabrication of Helical Copper Antennas

    NASA Technical Reports Server (NTRS)

    Petro, Andrew

    2006-01-01

    A simplified technique has been devised for fabricating helical antennas for use in experiments on radio-frequency generation and acceleration of plasmas. These antennas are typically made of copper (for electrical conductivity) and must have a specific helical shape and precise diameter.

  11. Fabrication of faceted nanopores in magnesium

    SciTech Connect

    Wu, Shujing; Cao, Fan; Zheng, He; Sheng, Huaping; Liu, Chun; Liu, Yu; Zhao, Dongshan; Wang, Jianbo

    2013-12-09

    In this paper, using high resolution transmission electron microscopy, we showed the fabrication of faceted nanopores with various shapes in magnesium by focused electron beam (e-beam). The characteristics of nanopore shapes and the crystallographic planes corresponding to the edges of the nanopores were discussed in detail. Interestingly, by manipulating the e-beam (e.g., irradiation direction and duration), the nanopore shape and size could be effectively controlled along different directions. Our results provide important insight into the nanopore patterning in metallic materials and are of fundamental importance concerning the relevant applications, such as nanopore-based sensor, etc.

  12. A stochastic convolution/superposition method with isocenter sampling to evaluate intrafraction motion effects in IMRT

    SciTech Connect

    Naqvi, Shahid A.; D'Souza, Warren D.

    2005-04-01

    Current methods to calculate dose distributions with organ motion can be broadly classified as 'dose convolution' and 'fluence convolution' methods. In the former, a static dose distribution is convolved with the probability distribution function (PDF) that characterizes the motion. However, artifacts are produced near the surface and around inhomogeneities because the method assumes shift invariance. Fluence convolution avoids these artifacts by convolving the PDF with the incident fluence instead of the patient dose. In this paper we present an alternative method that improves the accuracy, generality as well as the speed of dose calculation with organ motion. The algorithm starts by sampling an isocenter point from a parametrically defined space curve corresponding to the patient-specific motion trajectory. Then a photon is sampled in the linac head and propagated through the three-dimensional (3-D) collimator structure corresponding to a particular MLC segment chosen randomly from the planned IMRT leaf sequence. The photon is then made to interact at a point in the CT-based simulation phantom. Randomly sampled monoenergetic kernel rays issued from this point are then made to deposit energy in the voxels. Our method explicitly accounts for MLC-specific effects (spectral hardening, tongue-and-groove, head scatter) as well as changes in SSD with isocentric displacement, assuming that the body moves rigidly with the isocenter. Since the positions are randomly sampled from a continuum, there is no motion discretization, and the computation takes no more time than a static calculation. To validate our method, we obtained ten separate film measurements of an IMRT plan delivered on a phantom moving sinusoidally, with each fraction starting with a random phase. For 2 cm motion amplitude, we found that a ten-fraction average of the film measurements gave an agreement with the calculated infinite fraction average to within 2 mm in the isodose curves. The results also

  13. Real-time dose computation: GPU-accelerated source modeling and superposition/convolution

    SciTech Connect

    Jacques, Robert; Wong, John; Taylor, Russell; McNutt, Todd

    2011-01-15

    Purpose: To accelerate dose calculation to interactive rates using highly parallel graphics processing units (GPUs). Methods: The authors have extended their prior work in GPU-accelerated superposition/convolution with a modern dual-source model and have enhanced performance. The primary source algorithm supports both focused leaf ends and asymmetric rounded leaf ends. The extra-focal algorithm uses a discretized, isotropic area source and models multileaf collimator leaf height effects. The spectral and attenuation effects of static beam modifiers were integrated into each source's spectral function. The authors introduce the concepts of arc superposition and delta superposition. Arc superposition utilizes separate angular sampling for the total energy released per unit mass (TERMA) and superposition computations to increase accuracy and performance. Delta superposition allows single beamlet changes to be computed efficiently. The authors extended their concept of multi-resolution superposition to include kernel tilting. Multi-resolution superposition approximates solid angle ray-tracing, improving performance and scalability with a minor loss in accuracy. Superposition/convolution was implemented using the inverse cumulative-cumulative kernel and exact radiological path ray-tracing. The accuracy analyses were performed using multiple kernel ray samplings, both with and without kernel tilting and multi-resolution superposition. Results: Source model performance was <9 ms (data dependent) for a high resolution (400{sup 2}) field using an NVIDIA (Santa Clara, CA) GeForce GTX 280. Computation of the physically correct multispectral TERMA attenuation was improved by a material centric approach, which increased performance by over 80%. Superposition performance was improved by {approx}24% to 0.058 and 0.94 s for 64{sup 3} and 128{sup 3} water phantoms; a speed-up of 101-144x over the highly optimized Pinnacle{sup 3} (Philips, Madison, WI) implementation. Pinnacle{sup 3

  14. Loading metal nanostructures on cotton fabrics as recyclable catalysts.

    PubMed

    Yang, Baocheng; Zhao, Chunmei; Xiao, Manda; Wang, Feng; Li, Chuanhao; Wang, Jianfang; Yu, Jimmy C

    2013-04-01

    Noble metal nanostructures of varying compositions and shapes are loaded on cotton fabrics. The fabric-supported metal nanostructures can function as effective catalysts for different liquid-phase catalytic reactions. They exhibit superior recyclability, with the catalytic activities remaining nearly unchanged even after ten cycles of catalytic reactions for all of the three tested reactions.

  15. Tolerancing an optical freeform surface: an optical fabricator's perspective

    NASA Astrophysics Data System (ADS)

    DeGroote Nelson, Jessica; Medicus, Kate; Frisch, Gregory

    2015-09-01

    Freeform optical shapes or optical surfaces that are designed with non-symmetric features are gaining popularity with lens designers and optical system integrators. Tolerances on a freeform optical design influence the optical fabrication process. Case studies and soft tolerance limits for easier fabrication will be discussed. This paper will also give a high level overview of a freeform optical fabrication process that includes generation, high speed VIBE polishing, sub-aperture figure correction and testing of freeform surfaces.

  16. ASIC-based architecture for the real-time computation of 2D convolution with large kernel size

    NASA Astrophysics Data System (ADS)

    Shao, Rui; Zhong, Sheng; Yan, Luxin

    2015-12-01

    Bidimensional convolution is a low-level processing algorithm of interest in many areas, but its high computational cost constrains the size of the kernels, especially in real-time embedded systems. This paper presents a hardware architecture for the ASIC-based implementation of 2-D convolution with medium-large kernels. Aiming to improve the efficiency of storage resources on-chip, reducing off-chip bandwidth of these two issues, proposed construction of a data cache reuse. Multi-block SPRAM to cross cached images and the on-chip ping-pong operation takes full advantage of the data convolution calculation reuse, design a new ASIC data scheduling scheme and overall architecture. Experimental results show that the structure can achieve 40× 32 size of template real-time convolution operations, and improve the utilization of on-chip memory bandwidth and on-chip memory resources, the experimental results show that the structure satisfies the conditions to maximize data throughput output , reducing the need for off-chip memory bandwidth.

  17. Special Features of the Author-Publication Relationship and a New Explanation of Lotka's Law Based on Convolution Theory.

    ERIC Educational Resources Information Center

    Egghe, L.

    1994-01-01

    Discusses structural differences between author-publication systems and journal-article systems, i.e., articles can have more than one author. Frequency functions are examined; and a new conceptual explanation of Lotka's Law, based on convolution theory, is proposed. (Contains eight references.) (LRW)

  18. On the biologically effective dose (BED)—using convolution for calculating the effects of repair: I. Analytical considerations

    NASA Astrophysics Data System (ADS)

    Gustafsson, Johan; Nilsson, Per; Sjögreen Gleisner, Katarina

    2013-03-01

    This work presents a new mathematical formulation of biologically effective dose (BED) for radiation therapy where effects of repair need to be considered. The formulation is based on the observation that the effects of repair, both during protracted irradiation and of incomplete repair between fractions, can be written using a convolution, i.e. {BED}(T)=\\int \

  19. Aquifer response to stream-stage and recharge variations. II. Convolution method and applications

    USGS Publications Warehouse

    Barlow, P.M.; DeSimone, L.A.; Moench, A.F.

    2000-01-01

    In this second of two papers, analytical step-response functions, developed in the companion paper for several cases of transient hydraulic interaction between a fully penetrating stream and a confined, leaky, or water-table aquifer, are used in the convolution integral to calculate aquifer heads, streambank seepage rates, and bank storage that occur in response to streamstage fluctuations and basinwide recharge or evapotranspiration. Two computer programs developed on the basis of these step-response functions and the convolution integral are applied to the analysis of hydraulic interaction of two alluvial stream-aquifer systems in the northeastern and central United States. These applications demonstrate the utility of the analytical functions and computer programs for estimating aquifer and streambank hydraulic properties, recharge rates, streambank seepage rates, and bank storage. Analysis of the water-table aquifer adjacent to the Blackstone River in Massachusetts suggests that the very shallow depth of water table and associated thin unsaturated zone at the site cause the aquifer to behave like a confined aquifer (negligible specific yield). This finding is consistent with previous studies that have shown that the effective specific yield of an unconfined aquifer approaches zero when the capillary fringe, where sediment pores are saturated by tension, extends to land surface. Under this condition, the aquifer's response is determined by elastic storage only. Estimates of horizontal and vertical hydraulic conductivity, specific yield, specific storage, and recharge for a water-table aquifer adjacent to the Cedar River in eastern Iowa, determined by the use of analytical methods, are in close agreement with those estimated by use of a more complex, multilayer numerical model of the aquifer. Streambank leakance of the semipervious streambank materials also was estimated for the site. The streambank-leakance parameter may be considered to be a general (or lumped

  20. Resting State EEG-based biometrics for individual identification using convolutional neural networks.

    PubMed

    Lan Ma; Minett, James W; Blu, Thierry; Wang, William S-Y

    2015-08-01

    Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88%) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms.

  1. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction

    PubMed Central

    Quan, Chanqin

    2016-01-01

    The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task. PMID:27493967

  2. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction.

    PubMed

    Hua, Lei; Quan, Chanqin

    2016-01-01

    The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task. PMID:27493967

  3. Convolution based method for calculating inputs from dendritic fields in a continuum model of the retina.

    PubMed

    Al Abed, Amr; Yin, Shijie; Suaning, Gregg J; Lovell, Nigel H; Dokos, Socrates

    2012-01-01

    Computational models are valuable tools that can be used to aid the design and test the efficacy of electrical stimulation strategies in prosthetic vision devices. In continuum models of retinal electrophysiology, the effective extracellular potential can be considered as an approximate measure of the electrotonic loading a neuron's dendritic tree exerts on the soma. A convolution based method is presented to calculate the local spatial average of the effective extracellular loading in retinal ganglion cells (RGCs) in a continuum model of the retina which includes an active RGC tissue layer. The method can be used to study the effect of the dendritic tree size on the activation of RGCs by electrical stimulation using a hexagonal arrangement of electrodes (hexpolar) placed in the suprachoroidal space.

  4. Error-Trellis Construction for Convolutional Codes Using Shifted Error/Syndrome-Subsequences

    NASA Astrophysics Data System (ADS)

    Tajima, Masato; Okino, Koji; Miyagoshi, Takashi

    In this paper, we extend the conventional error-trellis construction for convolutional codes to the case where a given check matrix H(D) has a factor Dl in some column (row). In the first case, there is a possibility that the size of the state space can be reduced using shifted error-subsequences, whereas in the second case, the size of the state space can be reduced using shifted syndrome-subsequences. The construction presented in this paper is based on the adjoint-obvious realization of the corresponding syndrome former HT(D). In the case where all the columns and rows of H(D) are delay free, the proposed construction is reduced to the conventional one of Schalkwijk et al. We also show that the proposed construction can equally realize the state-space reduction shown by Ariel et al. Moreover, we clarify the difference between their construction and that of ours using examples.

  5. Asymptotic solution for first and second order linear Volterra integro-differential equations with convolution kernels

    NASA Astrophysics Data System (ADS)

    Bologna, Mauro

    2010-09-01

    This paper addresses the problem of finding an asymptotic solution for first- and second-order integro-differential equations containing an arbitrary kernel, by evaluating the corresponding inverse Laplace and Fourier transforms. The aim of the paper is to go beyond the Tauberian theorem in the case of integral-differential equations which are widely used by the scientific community. The results are applied to the convolute form of the Lindblad equation setting generic conditions on the kernel in such a way as to generate a positive definite density matrix, and show that the structure of the eigenvalues of the correspondent Liouvillian operator plays a crucial role in determining the positivity of the density matrix.

  6. A nonvoxel-based dose convolution/superposition algorithm optimized for scalable GPU architectures

    SciTech Connect

    Neylon, J. Sheng, K.; Yu, V.; Low, D. A.; Kupelian, P.; Santhanam, A.; Chen, Q.

    2014-10-15

    Purpose: Real-time adaptive planning and treatment has been infeasible due in part to its high computational complexity. There have been many recent efforts to utilize graphics processing units (GPUs) to accelerate the computational performance and dose accuracy in radiation therapy. Data structure and memory access patterns are the key GPU factors that determine the computational performance and accuracy. In this paper, the authors present a nonvoxel-based (NVB) approach to maximize computational and memory access efficiency and throughput on the GPU. Methods: The proposed algorithm employs a ray-tracing mechanism to restructure the 3D data sets computed from the CT anatomy into a nonvoxel-based framework. In a process that takes only a few milliseconds of computing time, the algorithm restructured the data sets by ray-tracing through precalculated CT volumes to realign the coordinate system along the convolution direction, as defined by zenithal and azimuthal angles. During the ray-tracing step, the data were resampled according to radial sampling and parallel ray-spacing parameters making the algorithm independent of the original CT resolution. The nonvoxel-based algorithm presented in this paper also demonstrated a trade-off in computational performance and dose accuracy for different coordinate system configurations. In order to find the best balance between the computed speedup and the accuracy, the authors employed an exhaustive parameter search on all sampling parameters that defined the coordinate system configuration: zenithal, azimuthal, and radial sampling of the convolution algorithm, as well as the parallel ray spacing during ray tracing. The angular sampling parameters were varied between 4 and 48 discrete angles, while both radial sampling and parallel ray spacing were varied from 0.5 to 10 mm. The gamma distribution analysis method (γ) was used to compare the dose distributions using 2% and 2 mm dose difference and distance-to-agreement criteria

  7. A high-order fast method for computing convolution integral with smooth kernel

    SciTech Connect

    Qiang, Ji

    2009-09-28

    In this paper we report on a high-order fast method to numerically calculate convolution integral with smooth non-periodic kernel. This method is based on the Newton-Cotes quadrature rule for the integral approximation and an FFT method for discrete summation. The method can have an arbitrarily high-order accuracy in principle depending on the number of points used in the integral approximation and a computational cost of O(Nlog(N)), where N is the number of grid points. For a three-point Simpson rule approximation, the method has an accuracy of O(h{sup 4}), where h is the size of the computational grid. Applications of the Simpson rule based algorithm to the calculation of a one-dimensional continuous Gauss transform and to the calculation of a two-dimensional electric field from a charged beam are also presented.

  8. FAST-PT: a novel algorithm to calculate convolution integrals in cosmological perturbation theory

    NASA Astrophysics Data System (ADS)

    McEwen, Joseph E.; Fang, Xiao; Hirata, Christopher M.; Blazek, Jonathan A.

    2016-09-01

    We present a novel algorithm, FAST-PT, for performing convolution or mode-coupling integrals that appear in nonlinear cosmological perturbation theory. The algorithm uses several properties of gravitational structure formation—the locality of the dark matter equations and the scale invariance of the problem—as well as Fast Fourier Transforms to describe the input power spectrum as a superposition of power laws. This yields extremely fast performance, enabling mode-coupling integral computations fast enough to embed in Monte Carlo Markov Chain parameter estimation. We describe the algorithm and demonstrate its application to calculating nonlinear corrections to the matter power spectrum, including one-loop standard perturbation theory and the renormalization group approach. We also describe our public code (in Python) to implement this algorithm. The code, along with a user manual and example implementations, is available at https://github.com/JoeMcEwen/FAST-PT.

  9. Convolution-variation separation method for efficient modeling of optical lithography.

    PubMed

    Liu, Shiyuan; Zhou, Xinjiang; Lv, Wen; Xu, Shuang; Wei, Haiqing

    2013-07-01

    We propose a general method called convolution-variation separation (CVS) to enable efficient optical imaging calculations without sacrificing accuracy when simulating images for a wide range of process variations. The CVS method is derived from first principles using a series expansion, which consists of a set of predetermined basis functions weighted by a set of predetermined expansion coefficients. The basis functions are independent of the process variations and thus may be computed and stored in advance, while the expansion coefficients depend only on the process variations. Optical image simulations for defocus and aberration variations with applications in robust inverse lithography technology and lens aberration metrology have demonstrated the main concept of the CVS method.

  10. Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks.

    PubMed

    Yao, Kun; Parkhill, John

    2016-03-01

    We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from an input electron density. The output of the network is used as a nonlocal correction to conventional local and semilocal kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. The density which minimizes the total energy given by the functional is examined in detail. We identify several avenues to improve on this exploratory work, by reducing numerical noise and changing the structure of our functional. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models.

  11. Subject independent facial expression recognition with robust face detection using a convolutional neural network.

    PubMed

    Matsugu, Masakazu; Mori, Katsuhiko; Mitari, Yusuke; Kaneda, Yuji

    2003-01-01

    Reliable detection of ordinary facial expressions (e.g. smile) despite the variability among individuals as well as face appearance is an important step toward the realization of perceptual user interface with autonomous perception of persons. We describe a rule-based algorithm for robust facial expression recognition combined with robust face detection using a convolutional neural network. In this study, we address the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. The result shows reliable detection of smiles with recognition rate of 97.6% for 5600 still images of more than 10 subjects. The proposed algorithm demonstrated the ability to discriminate smiling from talking based on the saliency score obtained from voting visual cues. To the best of our knowledge, it is the first facial expression recognition model with the property of subject independence combined with robustness to variability in facial appearance.

  12. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction.

    PubMed

    Hua, Lei; Quan, Chanqin

    2016-01-01

    The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.

  13. DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields.

    PubMed

    Wang, Sheng; Weng, Shunyan; Ma, Jianzhu; Tang, Qingming

    2015-01-01

    Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields), to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors.

  14. Strategies for Effectively Visualizing a 3D Flow Using Volume Line Integral Convolution

    NASA Technical Reports Server (NTRS)

    Interrante, Victoria; Grosch, Chester

    1997-01-01

    This paper discusses strategies for effectively portraying 3D flow using volume line integral convolution. Issues include defining an appropriate input texture, clarifying the distinct identities and relative depths of the advected texture elements, and selectively highlighting regions of interest in both the input and output volumes. Apart from offering insights into the greater potential of 3D LIC as a method for effectively representing flow in a volume, a principal contribution of this work is the suggestion of a technique for generating and rendering 3D visibility-impeding 'halos' that can help to intuitively indicate the presence of depth discontinuities between contiguous elements in a projection and thereby clarify the 3D spatial organization of elements in the flow. The proposed techniques are applied to the visualization of a hot, supersonic, laminar jet exiting into a colder, subsonic coflow.

  15. Convolutional modeling of diffraction effects in pulse-echo ultrasound imaging

    PubMed Central

    Mast, T. Douglas

    2010-01-01

    A model is presented for pulse-echo imaging of three-dimensional, linear, weakly-scattering continuum media by ultrasound array transducers. The model accounts for the diffracted fields of focused array subapertures in both transmit and receive modes, multiple transmit and receive focal zones, frequency-dependent attenuation, and aberration caused by mismatched medium and beamformer sound speeds. For a given medium reflectivity function, computation of a B-scan requires evaluation of a depth-dependent transmit∕receive beam product, followed by two one-dimensional convolutions and a one-dimensional summation. Numerical results obtained using analytic expressions for transmit and receive beams agree favorably with measured B-scan images and speckle statistics. PMID:20815433

  16. A convolutional code-based sequence analysis model and its application.

    PubMed

    Liu, Xiao; Geng, Xiaoli

    2013-04-16

    A new approach for encoding DNA sequences as input for DNA sequence analysis is proposed using the error correction coding theory of communication engineering. The encoder was designed as a convolutional code model whose generator matrix is designed based on the degeneracy of codons, with a codon treated in the model as an informational unit. The utility of the proposed model was demonstrated through the analysis of twelve prokaryote and nine eukaryote DNA sequences having different GC contents. Distinct differences in code distances were observed near the initiation and termination sites in the open reading frame, which provided a well-regulated characterization of the DNA sequences. Clearly distinguished period-3 features appeared in the coding regions, and the characteristic average code distances of the analyzed sequences were approximately proportional to their GC contents, particularly in the selected prokaryotic organisms, presenting the potential utility as an added taxonomic characteristic for use in studying the relationships of living organisms.

  17. Analysis of similarity/dissimilarity of DNA sequences based on convolutional code model.

    PubMed

    Liu, Xiao; Tian, Feng Chun; Wang, Shi Yuan

    2010-02-01

    Based on the convolutional code model of error-correction coding theory, we propose an approach to characterize and compare DNA sequences with consideration of the effect of codon context. We construct an 8-component vector whose components are the normalized leading eigenvalues of the L/L and M/M matrices associated with the original DNA sequences and the transformed sequences. The utility of our approach is illustrated by the examination of the similarities/dissimilarities among the coding sequences of the first exon of beta-globin gene of 11 species, and the efficiency of error-correction coding theory in analysis of similarity/dissimilarity of DNA sequences is represented.

  18. A perceptual quantization strategy for HEVC based on a convolutional neural network trained on natural images

    NASA Astrophysics Data System (ADS)

    Alam, Md Mushfiqul; Nguyen, Tuan D.; Hagan, Martin T.; Chandler, Damon M.

    2015-09-01

    Fast prediction models of local distortion visibility and local quality can potentially make modern spatiotemporally adaptive coding schemes feasible for real-time applications. In this paper, a fast convolutional-neural- network based quantization strategy for HEVC is proposed. Local artifact visibility is predicted via a network trained on data derived from our improved contrast gain control model. The contrast gain control model was trained on our recent database of local distortion visibility in natural scenes [Alam et al. JOV 2014]. Further- more, a structural facilitation model was proposed to capture effects of recognizable structures on distortion visibility via the contrast gain control model. Our results provide on average 11% improvements in compression efficiency for spatial luma channel of HEVC while requiring almost one hundredth of the computational time of an equivalent gain control model. Our work opens the doors for similar techniques which may work for different forthcoming compression standards.

  19. Characterization of glucose transport by cultured rabbit kidney proximal convoluted and proximal straight tubule cells.

    PubMed

    Del Valle, Pedro L; Trifillis, Anna; Ruegg, Charles E; Kane, Andrew S

    2002-04-01

    Rabbit kidney proximal convoluted tubule (RPCT) and proximal straight tubule (RPST) cells were independently isolated and cultured. The kinetics of the sodium-dependent glucose transport was characterized by determining the uptake of the glucose analog alpha-methylglucopyranoside. Cell culture and assay conditions used in these experiments were based on previous experiments conducted on the renal cell line derived from the whole kidney of the Yorkshire pig (LLC-PK1). Results indicated the presence of two distinct sodium-dependent glucose transporters in rabbit renal cells: a relatively high-capacity, low-affinity transporter (V(max) = 2.28 +/- 0.099 nmoles/mg protein min, Km = 4.1 +/- 0.27 mM) in RPCT cells and a low-capacity, high-affinity transporter (V(max) = 0.45 +/- 0.076 nmoles/mg protein min, K(m) = 1.7 +/- 0.43 mM) in RPST cells. A relatively high-capacity, low-affinity transporter (V(max) = 1.68 +/- 0.215 nmoles/mg protein min, Km = 4.9 +/- 0.23 mM) was characterized in LLC-PK1 cells. Phlorizin inhibited the uptake of alpha-methylglucopyranoside in proximal convoluted, proximal straight, and LLC-PK1 cells by 90, 50, and 90%, respectively. Sodium-dependent glucose transport in all three cell types was specific for hexoses. These data are consistent with the kinetic heterogeneity of sodium-dependent glucose transport in the S1-S2 and S3 segments of the mammalian renal proximal tubule. The RPCT-RPST cultured cell model is novel, and this is the first report of sodium-dependent glucose transport characterization in primary cultures of proximal straight tubule cells. Our results support the use of cultured monolayers of RPCT and RPST cells as a model system to evaluate segment-specific differences in these renal cell types.

  20. FULLY CONVOLUTIONAL NETWORKS FOR MULTI-MODALITY ISOINTENSE INFANT BRAIN IMAGE SEGMENTATION

    PubMed Central

    Nie, Dong; Wang, Li; Gao, Yaozong; Shen, Dinggang

    2016-01-01

    The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In the isointense phase (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, resulting in extremely low tissue contrast and thus making the tissue segmentation very challenging. The existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single T1, T2 or fractional anisotropy (FA) modality or their simply-stacked combinations without fully exploring the multi-modality information. To address the challenge, in this paper, we propose to use fully convolutional networks (FCNs) for the segmentation of isointense phase brain MR images. Instead of simply stacking the three modalities, we train one network for each modality image, and then fuse their high-layer features together for final segmentation. Specifically, we conduct a convolution-pooling stream for multimodality information from T1, T2, and FA images separately, and then combine them in high-layer for finally generating the segmentation maps as the outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense phase brain images. Results showed that our proposed model significantly outperformed previous methods in terms of accuracy. In addition, our results also indicated a better way of integrating multi-modality images, which leads to performance improvement.

  1. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

    PubMed

    Shin, Hoo-Chang; Roth, Holger R; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel; Summers, Ronald M

    2016-05-01

    Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks. PMID:26886976

  2. Reducing weight precision of convolutional neural networks towards large-scale on-chip image recognition

    NASA Astrophysics Data System (ADS)

    Ji, Zhengping; Ovsiannikov, Ilia; Wang, Yibing; Shi, Lilong; Zhang, Qiang

    2015-05-01

    In this paper, we develop a server-client quantization scheme to reduce bit resolution of deep learning architecture, i.e., Convolutional Neural Networks, for image recognition tasks. Low bit resolution is an important factor in bringing the deep learning neural network into hardware implementation, which directly determines the cost and power consumption. We aim to reduce the bit resolution of the network without sacrificing its performance. To this end, we design a new quantization algorithm called supervised iterative quantization to reduce the bit resolution of learned network weights. In the training stage, the supervised iterative quantization is conducted via two steps on server - apply k-means based adaptive quantization on learned network weights and retrain the network based on quantized weights. These two steps are alternated until the convergence criterion is met. In this testing stage, the network configuration and low-bit weights are loaded to the client hardware device to recognize coming input in real time, where optimized but expensive quantization becomes infeasible. Considering this, we adopt a uniform quantization for the inputs and internal network responses (called feature maps) to maintain low on-chip expenses. The Convolutional Neural Network with reduced weight and input/response precision is demonstrated in recognizing two types of images: one is hand-written digit images and the other is real-life images in office scenarios. Both results show that the new network is able to achieve the performance of the neural network with full bit resolution, even though in the new network the bit resolution of both weight and input are significantly reduced, e.g., from 64 bits to 4-5 bits.

  3. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.

    PubMed

    Kleesiek, Jens; Urban, Gregor; Hubert, Alexander; Schwarz, Daniel; Maier-Hein, Klaus; Bendszus, Martin; Biller, Armin

    2016-04-01

    Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging workflows. Current methods demonstrate good results on non-enhanced T1-weighted images, but struggle when confronted with other modalities and pathologically altered tissue. In this paper we present a 3D convolutional deep learning architecture to address these shortcomings. In contrast to existing methods, we are not limited to non-enhanced T1w images. When trained appropriately, our approach handles an arbitrary number of modalities including contrast-enhanced scans. Its applicability to MRI data, comprising four channels: non-enhanced and contrast-enhanced T1w, T2w and FLAIR contrasts, is demonstrated on a challenging clinical data set containing brain tumors (N=53), where our approach significantly outperforms six commonly used tools with a mean Dice score of 95.19. Further, the proposed method at least matches state-of-the-art performance as demonstrated on three publicly available data sets: IBSR, LPBA40 and OASIS, totaling N=135 volumes. For the IBSR (96.32) and LPBA40 (96.96) data set the convolutional neuronal network (CNN) obtains the highest average Dice scores, albeit not being significantly different from the second best performing method. For the OASIS data the second best Dice (95.02) results are achieved, with no statistical difference in comparison to the best performing tool. For all data sets the highest average specificity measures are evaluated, whereas the sensitivity displays about average results. Adjusting the cut-off threshold for generating the binary masks from the CNN's probability output can be used to increase the sensitivity of the method. Of course, this comes at the cost of a decreased specificity and has to be decided application specific. Using an optimized GPU implementation predictions can be achieved in less than one minute. The proposed method may prove useful for large-scale studies and clinical trials. PMID:26808333

  4. Deep convolutional networks for automated detection of posterior-element fractures on spine CT

    NASA Astrophysics Data System (ADS)

    Roth, Holger R.; Wang, Yinong; Yao, Jianhua; Lu, Le; Burns, Joseph E.; Summers, Ronald M.

    2016-03-01

    Injuries of the spine, and its posterior elements in particular, are a common occurrence in trauma patients, with potentially devastating consequences. Computer-aided detection (CADe) could assist in the detection and classification of spine fractures. Furthermore, CAD could help assess the stability and chronicity of fractures, as well as facilitate research into optimization of treatment paradigms. In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine. First, the vertebra bodies of the spine with its posterior elements are segmented in spine CT using multi-atlas label fusion. Then, edge maps of the posterior elements are computed. These edge maps serve as candidate regions for predicting a set of probabilities for fractures along the image edges using ConvNets in a 2.5D fashion (three orthogonal patches in axial, coronal and sagittal planes). We explore three different methods for training the ConvNet using 2.5D patches along the edge maps of `positive', i.e. fractured posterior-elements and `negative', i.e. non-fractured elements. An experienced radiologist retrospectively marked the location of 55 displaced posterior-element fractures in 18 trauma patients. We randomly split the data into training and testing cases. In testing, we achieve an area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at 5 or 10 false-positives per patient, respectively. Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.

  5. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

    PubMed

    Ypsilantis, Petros-Pavlos; Siddique, Musib; Sohn, Hyon-Mok; Davies, Andrew; Cook, Gary; Goh, Vicky; Montana, Giovanni

    2015-01-01

    Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a "radiomics" approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models.

  6. Efficient training algorithms for a class of shunting inhibitory convolutional neural networks.

    PubMed

    Tivive, Fok Hing Chi; Bouzerdoum, Abdesselam

    2005-05-01

    This article presents some efficient training algorithms, based on first-order, second-order, and conjugate gradient optimization methods, for a class of convolutional neural networks (CoNNs), known as shunting inhibitory convolution neural networks. Furthermore, a new hybrid method is proposed, which is derived from the principles of Quickprop, Rprop, SuperSAB, and least squares (LS). Experimental results show that the new hybrid method can perform as well as the Levenberg-Marquardt (LM) algorithm, but at a much lower computational cost and less memory storage. For comparison sake, the visual pattern recognition task of face/nonface discrimination is chosen as a classification problem to evaluate the performance of the training algorithms. Sixteen training algorithms are implemented for the three different variants of the proposed CoNN architecture: binary-, Toeplitz- and fully connected architectures. All implemented algorithms can train the three network architectures successfully, but their convergence speed vary markedly. In particular, the combination of LS with the new hybrid method and LS with the LM method achieve the best convergence rates in terms of number of training epochs. In addition, the classification accuracies of all three architectures are assessed using ten-fold cross validation. The results show that the binary- and Toeplitz-connected architectures outperform slightly the fully connected architecture: the lowest error rates across all training algorithms are 1.95% for Toeplitz-connected, 2.10% for the binary-connected, and 2.20% for the fully connected network. In general, the modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods, the three variants of LM algorithm, and the new hybrid/LS method perform consistently well, achieving error rates of less than 3% averaged across all three architectures.

  7. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.

    PubMed

    Kleesiek, Jens; Urban, Gregor; Hubert, Alexander; Schwarz, Daniel; Maier-Hein, Klaus; Bendszus, Martin; Biller, Armin

    2016-04-01

    Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging workflows. Current methods demonstrate good results on non-enhanced T1-weighted images, but struggle when confronted with other modalities and pathologically altered tissue. In this paper we present a 3D convolutional deep learning architecture to address these shortcomings. In contrast to existing methods, we are not limited to non-enhanced T1w images. When trained appropriately, our approach handles an arbitrary number of modalities including contrast-enhanced scans. Its applicability to MRI data, comprising four channels: non-enhanced and contrast-enhanced T1w, T2w and FLAIR contrasts, is demonstrated on a challenging clinical data set containing brain tumors (N=53), where our approach significantly outperforms six commonly used tools with a mean Dice score of 95.19. Further, the proposed method at least matches state-of-the-art performance as demonstrated on three publicly available data sets: IBSR, LPBA40 and OASIS, totaling N=135 volumes. For the IBSR (96.32) and LPBA40 (96.96) data set the convolutional neuronal network (CNN) obtains the highest average Dice scores, albeit not being significantly different from the second best performing method. For the OASIS data the second best Dice (95.02) results are achieved, with no statistical difference in comparison to the best performing tool. For all data sets the highest average specificity measures are evaluated, whereas the sensitivity displays about average results. Adjusting the cut-off threshold for generating the binary masks from the CNN's probability output can be used to increase the sensitivity of the method. Of course, this comes at the cost of a decreased specificity and has to be decided application specific. Using an optimized GPU implementation predictions can be achieved in less than one minute. The proposed method may prove useful for large-scale studies and clinical trials.

  8. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

    PubMed

    Shin, Hoo-Chang; Roth, Holger R; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel; Summers, Ronald M

    2016-05-01

    Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

  9. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks

    PubMed Central

    Ypsilantis, Petros-Pavlos; Siddique, Musib; Sohn, Hyon-Mok; Davies, Andrew; Cook, Gary; Goh, Vicky; Montana, Giovanni

    2015-01-01

    Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient’s response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a “radiomics” approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models. PMID:26355298

  10. Ceramic for Silicon-Shaping Dies

    NASA Technical Reports Server (NTRS)

    Sekercioglu, I.; Wills, R. R.

    1982-01-01

    Silicon beryllium oxynitride (SiBON) is a promising candidate material for manufacture of shaping dies used in fabricating ribbons or sheets of silicon. It is extremely stable, resists thermal shock, and has excellent resistance to molten silicon. SiBON is a solid solution of beryllium silicate in beta-silicon nitride.

  11. Robust Vacuum-/Air-Dried Graphene Aerogels and Fast Recoverable Shape-Memory Hybrid Foams.

    PubMed

    Li, Chenwei; Qiu, Ling; Zhang, Baoqing; Li, Dan; Liu, Chen-Yang

    2016-02-17

    New graphene aerogels can be fabricated by vacuum/air drying, and because of the mechanical robustness of the graphene aerogels, shape-memory polymer/graphene hybrid foams can be fabricated by a simple infiltration-air-drying-crosslinking method. Due to the superelasticity, high strength, and good electrical conductivity of the as-prepared graphene aerogels, the shape-memory hybrid foams exhibit excellent thermotropical and electrical shape-memory properties, outperforming previously reported shape-memory polymer foams.

  12. Shape measurement biases from underfitting and ellipticity gradients

    SciTech Connect

    Bernstein, Gary M.

    2010-08-21

    With this study, precision weak gravitational lensing experiments require measurements of galaxy shapes accurate to <1 part in 1000. We investigate measurement biases, noted by Voigt and Bridle (2009) and Melchior et al. (2009), that are common to shape measurement methodologies that rely upon fitting elliptical-isophote galaxy models to observed data. The first bias arises when the true galaxy shapes do not match the models being fit. We show that this "underfitting bias" is due, at root, to these methods' attempts to use information at high spatial frequencies that has been destroyed by the convolution with the point-spread function (PSF) and/or by sampling. We propose a new shape-measurement technique that is explicitly confined to observable regions of k-space. A second bias arises for galaxies whose ellipticity varies with radius. For most shape-measurement methods, such galaxies are subject to "ellipticity gradient bias". We show how to reduce such biases by factors of 20–100 within the new shape-measurement method. The resulting shear estimator has multiplicative errors < 1 part in 103 for high-S/N images, even for highly asymmetric galaxies. Without any training or recalibration, the new method obtains Q = 3000 in the GREAT08 Challenge of blind shear reconstruction on low-noise galaxies, several times better than any previous method.

  13. Shape measurement biases from underfitting and ellipticity gradients

    DOE PAGESBeta

    Bernstein, Gary M.

    2010-08-21

    With this study, precision weak gravitational lensing experiments require measurements of galaxy shapes accurate to <1 part in 1000. We investigate measurement biases, noted by Voigt and Bridle (2009) and Melchior et al. (2009), that are common to shape measurement methodologies that rely upon fitting elliptical-isophote galaxy models to observed data. The first bias arises when the true galaxy shapes do not match the models being fit. We show that this "underfitting bias" is due, at root, to these methods' attempts to use information at high spatial frequencies that has been destroyed by the convolution with the point-spread function (PSF)more » and/or by sampling. We propose a new shape-measurement technique that is explicitly confined to observable regions of k-space. A second bias arises for galaxies whose ellipticity varies with radius. For most shape-measurement methods, such galaxies are subject to "ellipticity gradient bias". We show how to reduce such biases by factors of 20–100 within the new shape-measurement method. The resulting shear estimator has multiplicative errors < 1 part in 103 for high-S/N images, even for highly asymmetric galaxies. Without any training or recalibration, the new method obtains Q = 3000 in the GREAT08 Challenge of blind shear reconstruction on low-noise galaxies, several times better than any previous method.« less

  14. Rapid fabrication of materials using directed light fabrication

    SciTech Connect

    Thoma, D.J.; Lewis, G.K.; Milewski, J.O.; Chen, K.C.; Nemec, R.B.

    1997-10-01

    Directed light fabrication (DLF) is a rapid fabrication process that fuses gas delivered metal powders within a focal zone of a laser beam to produce fully dense, near-net shape, 3-dimensional metal components from a computer generated solid model. Computer controls dictate the metal deposition pathways, and no preforms or molds are required to generate complex sample geometries. The focal zone of the laser beam is programmed to move along or across a part cross-section, and coupled with a multi-axis sample stage, produces the desired part. By maintaining a constant molten puddle within the focal zone, a continuous liquid/solid interface is possible while achieving constant cooling rates that can be varied between 10 to 10{sup 4} K s{sup -1} and solidification growth rates (that scale with the beam velocity) ranging up to 10{sup 2} m s{sup -1}. The DLF technique offers unique advantages over conventional thermomechanical processes in that many labor and equipment intensive steps can be avoided. Moreover, owing to the flexibility in power distributions of lasers, a variety of materials can be processed, ranging from aluminum alloys to rhenium, and including intermetallics such as Mo{sub 5}Si{sub 3}. As a result, the rapid fabrication of conventional and advanced materials are possible.

  15. Deep Neural Networks as a Computational Model for Human Shape Sensitivity

    PubMed Central

    Op de Beeck, Hans P.

    2016-01-01

    Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented, and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional ‘deep’ neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development. PMID:27124699

  16. Fabrication of sinterable silicon nitride by injection molding

    NASA Technical Reports Server (NTRS)

    Quackenbush, C. L.; French, K.; Neil, J. T.

    1982-01-01

    Transformation of structural ceramics from the laboratory to production requires development of near net shape fabrication techniques which minimize finish grinding. One potential technique for producing large quantities of complex-shaped parts at a low cost, and microstructure of sintered silicon nitride fabricated by injection molding is discussed and compared to data generated from isostatically dry-pressed material. Binder selection methodology, compounding of ceramic and binder components, injection molding techniques, and problems in binder removal are discussed. Strength, oxidation resistance, and microstructure of sintered silicon nitride fabricated by injection molding is discussed and compared to data generated from isostatically dry-pressed material.

  17. Photochemical cutting of fabrics

    DOEpatents

    Piltch, Martin S.

    1994-01-01

    Apparatus for the cutting of garment patterns from one or more layers of fabric. A laser capable of producing laser light at an ultraviolet wavelength is utilized to shine light through a pattern, such as a holographic phase filter, and through a lens onto the one or more layers of fabric. The ultraviolet laser light causes rapid photochemical decomposition of the one or more layers of fabric, but only along the pattern. The balance of the fabric of the one or more layers of fabric is undamaged.

  18. Flexible Metal-Fabric Radiators

    NASA Technical Reports Server (NTRS)

    Cross, Cynthia; Nguyen, Hai D.; Ruemmele, Warren; Andish, Kambiz K.; McCalley, Sean

    2005-01-01

    Flexible metal-fabric radiators have been considered as alternative means of dissipating excess heat from spacecraft and space suits. The radiators also may be useful in such special terrestrial applications as rejecting heat from space-suit-like protective suits worn in hot work environments. In addition to flexibility and consequent ease of deployment and installation on objects of varying sizes and shapes, the main advantages of these radiators over conventional rigid radiators are that they weigh less and occupy less volume for a given amount of cooling capacity. A radiator of this type includes conventional stainless-steel tubes carrying a coolant fluid. The main radiating component consists of a fabric of interwoven aluminum-foil strips bonded to the tubes by use of a proprietary process. The strip/tube bonds are strong and highly thermally conductive. Coolant is fed to and from the tubes via flexible stainless-steel manifolds designed to accommodate flexing of, and minimize bending forces on, the fabric. The manifolds are sized to minimize pressure drops and distribute the flow of coolant evenly to all the tubes. The tubes and manifolds are configured in two independent flow loops for operational flexibility and protective redundancy.

  19. Shape-Shifting Plastic

    SciTech Connect

    2015-05-20

    A new plastic developed by ORNL and Washington State University transforms from its original shape through a series of temporary shapes and returns to its initial form. The shape-shifting process is controlled through changes in temperature

  20. Method for Fabricating Composite Structures Using Pultrusion Processing

    NASA Technical Reports Server (NTRS)

    Farley, Gary L. (Inventor)

    2000-01-01

    A method for fabricating composite structures at a low-cost, moderate-to-high production rate. A first embodiment of the method includes employing a continuous press forming fabrication process. A second embodiment of the method includes employing a pultrusion process for obtaining composite structures. The methods include coating yarns with matrix material, weaving the yarn into fabric to produce a continuous fabric supply and feeding multiple layers of net-shaped fabrics having optimally oriented fibers into a debulking tool to form an undebulked preform. The continuous press forming fabrication process includes partially debulking the preform, cutting the partially debulked preform and debulking the partially debulked preform to form a net-shape. An electron-beam or similar technique then cures the structure. The pultrusion fabric process includes feeding the undebulked preform into a heated die and gradually debulking the undebulked preform. The undebulked preform in the heated die changes dimension until a desired cross-sectional dimension is achieved. This process further includes obtaining a net-shaped infiltrated uncured preform, cutting the uncured preform to a desired length and electron-beam curing (or similar technique) the uncured preform. These fabrication methods produce superior structures formed at higher production rates, resulting in lower cost and high structural performance.

  1. Tooth - abnormal shape

    MedlinePlus

    Hutchinson incisors; Abnormal tooth shape; Peg teeth; Mulberry teeth; Conical teeth ... The appearance of normal teeth varies, especially the molars. ... conditions. Specific diseases can affect tooth shape, tooth ...

  2. Seismic body wave separation in volcano-tectonic activity inferred by the Convolutive Independent Component Analysis

    NASA Astrophysics Data System (ADS)

    Capuano, Paolo; De Lauro, Enza; De Martino, Salvatore; Falanga, Mariarosaria; Petrosino, Simona

    2015-04-01

    One of the main challenge in volcano-seismological literature is to locate and characterize the source of volcano/tectonic seismic activity. This passes through the identification at least of the onset of the main phases, i.e. the body waves. Many efforts have been made to solve the problem of a clear separation of P and S phases both from a theoretical point of view and developing numerical algorithms suitable for specific cases (see, e.g., Küperkoch et al., 2012). Recently, a robust automatic procedure has been implemented for extracting the prominent seismic waveforms from continuously recorded signals and thus allowing for picking the main phases. The intuitive notion of maximum non-gaussianity is achieved adopting techniques which involve higher-order statistics in frequency domain., i.e, the Convolutive Independent Component Analysis (CICA). This technique is successful in the case of the blind source separation of convolutive mixtures. In seismological framework, indeed, seismic signals are thought as the convolution of a source function with path, site and the instrument response. In addition, time-delayed versions of the same source exist, due to multipath propagation typically caused by reverberations from some obstacle. In this work, we focus on the Volcano Tectonic (VT) activity at Campi Flegrei Caldera (Italy) during the 2006 ground uplift (Ciaramella et al., 2011). The activity was characterized approximately by 300 low-magnitude VT earthquakes (Md < 2; for the definition of duration magnitude, see Petrosino et al. 2008). Most of them were concentrated in distinct seismic sequences with hypocenters mainly clustered beneath the Solfatara-Accademia area, at depths ranging between 1 and 4 km b.s.l.. The obtained results show the clear separation of P and S phases: the technique not only allows the identification of the S-P time delay giving the timing of both phases but also provides the independent waveforms of the P and S phases. This is an enormous

  3. Energy-beam-driven rapid fabrication system

    DOEpatents

    Keicher, David M.; Atwood, Clinton L.; Greene, Donald L.; Griffith, Michelle L.; Harwell, Lane D.; Jeantette, Francisco P.; Romero, Joseph A.; Schanwald, Lee P.; Schmale, David T.

    2002-01-01

    An energy beam driven rapid fabrication system, in which an energy beam strikes a growth surface to form a molten puddle thereon. Feed powder is then injected into the molten puddle from a converging flow of feed powder. A portion of the feed powder becomes incorporated into the molten puddle, forcing some of the puddle contents to freeze on the growth surface, thereby adding an additional layer of material. By scanning the energy beam and the converging flow of feed powder across the growth surface, complex three-dimensional shapes can be formed, ready or nearly ready for use. Nearly any class of material can be fabricated using this system.

  4. Fabrication of a fluidic membrane lens system

    NASA Astrophysics Data System (ADS)

    Draheim, J.; Schneider, F.; Kamberger, R.; Mueller, C.; Wallrabe, U.

    2009-09-01

    We present the fabrication process of a fluidic membrane lens system with an integrated piezoelectric pumping actuator. The optical unit and the pumping unit are fabricated through casting using a hot embossing machine. Two different systems, one with a homogeneous membrane thickness, and one with an inhomogeneous membrane thickness distribution, are manufactured. The influence of the volume shrinkage of the silicone during curing on the membrane shape and on the focal length is analyzed. The assembled system achieves a focal length between +52.4 mm and -70.9 mm at a piezovoltage of ±40 V. The full-scale response time of the system is below 24 ms.

  5. Detailed investigation of Long-Period activity at Campi Flegrei by Convolutive Independent Component Analysis

    NASA Astrophysics Data System (ADS)

    Capuano, P.; De Lauro, E.; De Martino, S.; Falanga, M.

    2016-04-01

    This work is devoted to the analysis of seismic signals continuously recorded at Campi Flegrei Caldera (Italy) during the entire year 2006. The radiation pattern associated with the Long-Period energy release is investigated. We adopt an innovative Independent Component Analysis algorithm for convolutive seismic series adapted and improved to give automatic procedures for detecting seismic events often buried in the high-level ambient noise. The extracted waveforms characterized by an improved signal-to-noise ratio allows the recognition of Long-Period precursors, evidencing that the seismic activity accompanying the mini-uplift crisis (in 2006), which climaxed in the three days from 26-28 October, had already started at the beginning of the month of October and lasted until mid of November. Hence, a more complete seismic catalog is then provided which can be used to properly quantify the seismic energy release. To better ground our results, we first check the robustness of the method by comparing it with other blind source separation methods based on higher order statistics; secondly, we reconstruct the radiation patterns of the extracted Long-Period events in order to link the individuated signals directly to the sources. We take advantage from Convolutive Independent Component Analysis that provides basic signals along the three directions of motion so that a direct polarization analysis can be performed with no other filtering procedures. We show that the extracted signals are mainly composed of P waves with radial polarization pointing to the seismic source of the main LP swarm, i.e. a small area in the Solfatara, also in the case of the small-events, that both precede and follow the main activity. From a dynamical point of view, they can be described by two degrees of freedom, indicating a low-level of complexity associated with the vibrations from a superficial hydrothermal system. Our results allow us to move towards a full description of the complexity of

  6. Modulation of phosphate absorption by calcium in the rabbit proximal convoluted tubule.

    PubMed Central

    Rouse, D; Suki, W N

    1985-01-01

    Proximal convoluted (S2) and straight (S3) renal tubule segments were studied to determine the effect of Ca on lumen-to-bath phosphate flux (JlbPO4). Increasing bath and perfusate Ca from 1.8 to 3.6 mM enhanced JlbPO4 from 3.3 +/- 0.7 to 6.6 +/- 0.6 pmol/mm per min in S2 segments (P less than 0.001) but had no effect in S3 segments. Decreasing bath and perfusate Ca from 1.8 to 0.2 mM reduced JlbPO4 from 3.7 +/- 0.6 to 2.2 +/- 0.6 in S2 segments. These effects were unrelated to changes in fluid absorption and transepithelial potential difference. Increasing cytosolic Ca with a Ca ionophore, inhibiting the Ca-calmodulin complex with trifluoperazine, or applying the Ca channel blocker nifedipine had no effect on JlBPO4 in S2 segments. Increasing only bath Ca from 1.8 to 3.6 mM did not significantly affect JlbPO4. However, increasing only perfusate Ca enhanced JlbPO4 from 3.4 +/- 0.7 to 6.1 +/- 0.7 pmol/mm per min (P less than 0.005). Inhibition of hydrogen ion secretion, by using a low bicarbonate, low pH perfusate, both depressed base-line JlbPO4 and abolished the stimulatory effect of raising perfusate Ca. Net phosphate efflux (JnetPO4) also increased after ambient calcium levels were raised, ruling out a significant increase in PO4 backflux. When net sodium transport was abolished by reducing the bath temperature to 24 degrees C, JnetPO4 at normal ambient calcium was reduced and increasing ambient calcium failed to increase it, ruling out a simple physicochemical reaction wherein phosphate precipitates out of solution with calcium. The present studies provide direct evidence for a stimulatory effect of Ca on sodium-dependent PO4 absorption in the proximal convoluted tubule, exerted at the luminal membrane. It is postulated that Ca modulates the affinity of the PO4 transporter for the anion. PMID:4031067

  7. Overprints of magnetic fabric: A review

    NASA Astrophysics Data System (ADS)

    Hrouda, Frantisek; Chadima, Martin

    2016-04-01

    The magnetic fabrics in sedimentary, volcanic, and plutonic rocks primarily originate during deposition, lava or ash flow, and magma flow, respectively. During later rock development, these magnetic fabrics can be overprinted by various processes among which regional metamorphism and ductile deformation tectonic in origin are probably the most frequent and important. Because of the second rank tensor character of the anisotropy of magnetic susceptibility (AMS) it is often difficult to recognize whether a particular magnetic fabric was overprinted or not. The primary magnetic fabric of sedimentary rocks has only limited variability despite of possibly large variability of the deposition conditions. The degree of AMS is relatively low (usually P<1.05), the AMS ellipsoid is always planar (T>0) and the magnetic foliation is near the bedding. The magnetic lineation is mostly parallel to the direction of near-bottom water current, while in some cases it can be perpendicular (for example in turbidites of the A member of the Bouma cycle). Even since its first investigations into volcanic rocks in the early sixties, the AMS has been considered to reflect the preferred orientation of titanomagnetite grains by grain shape produced by lava flow and the magnetic fabric is then conformable to the shapes of the volcanic bodies. In lava flows, sills, dykes, and other tabular bodies, the magnetic foliation is approximately parallel to the panel and the magnetic lineation is often parallel to the flow direction even though it can also be perpendicular. The magnetic fabric in granitic rocks primarily originates during the emplacement of these rocks into the upper layers of the Earth's crust. They are usually characterized by the conformity of the magnetic fabric with the intrusion-induced mesoscopic fabric elements, if observable, and/or with the shapes of magmatic bodies, and by the relatively low degree of AMS. During metamorphic and/or tectonic overprint, the degree of AMS in

  8. Quantifying the effect of rheology on plan-view shapes of lava flows

    NASA Technical Reports Server (NTRS)

    Bruno, B. C.; Taylor, G. J.; Lopes-Gautier, R. M. C.

    1993-01-01

    This study aims at quantifying the effect of rheology on the plan-view shapes of lava flows. Plan-view shapes of lava flows are important because they reflect the processes governing flow emplacement and may provide insight into lava flow rheology and dynamics. In our earlier investigation, it was reported that plan-view shapes of tholeite basalts are fractal, having a characteristic shape regardless of scale. It was also found one could use the fractal dimension (a parameter which quantifies flow margin convolution) to distinguish between the two major types of basalts: a'a and pahoehoe. Encouraged by these earlier results, a similar method for use on silicic flows are being developed and our preliminary work is presented.

  9. Spatial-Spectral Classification Based on the Unsupervised Convolutional Sparse Auto-Encoder for Hyperspectral Remote Sensing Imagery

    NASA Astrophysics Data System (ADS)

    Han, Xiaobing; Zhong, Yanfei; Zhang, Liangpei

    2016-06-01

    Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets - Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

  10. Fabrics for aeronautic construction

    NASA Technical Reports Server (NTRS)

    Walen, E D

    1918-01-01

    The Bureau of Standards undertook the investigation of airplane fabrics with the view of finding suitable substitutes for the linen fabrics, and it was decided that the fibers to be considered were cotton, ramie, silk, and hemp. Of these, the cotton fiber was the logical one to be given primary consideration. Report presents the suitability, tensibility and stretching properties of cotton fabric obtained by laboratory tests.

  11. Tactile Fabric Panel in an Eight Zones Structure

    PubMed Central

    Alsina, Maria; Escudero, Francesc; Margalef, Jordi; Luengo, Sonia

    2007-01-01

    By introducing a percentage of conductive material during the manufacture of sewing thread, it is possible to obtain a fabric which is able to detect variations in pressure in certain areas. In previous experiments the existence of resistance variations has been demonstrated, although some constrains of cause and effect were found in the fabric. The research has been concentrated in obtaining a fabric that allows electronic detection of its shape changes. Additionally, and because a causal behavior is needed, it is necessary that the fabric recovers its original shape when the external forces cease. The structure of the fabric varies with the type of deformation applied. Two kinds of deformation are described: those caused by stretching and those caused by pressure. This last type of deformation gives different responses depending on the conductivity of the object used to cause the pressure. This effect is related to the type of thread used to manufacture the fabric. So, if the pressure is caused by a finger the response is different compared to the response caused by a conductive object. Another fact that has to be mentioned is that a pressure in a specific point of the fabric can affect other detection points depending on the force applied. This effect is related to the fabric structure. The goals of this article are validating the structure of the fabric used, as well as the study of the two types of deformation mentioned before.

  12. Application of the Convolution Formalism to the Ocean Tide Potential: Results from the Gravity and Recovery and Climate Experiment (GRACE)

    NASA Technical Reports Server (NTRS)

    Desai, S. D.; Yuan, D. -N.

    2006-01-01

    A computationally efficient approach to reducing omission errors in ocean tide potential models is derived and evaluated using data from the Gravity Recovery and Climate Experiment (GRACE) mission. Ocean tide height models are usually explicitly available at a few frequencies, and a smooth unit response is assumed to infer the response across the tidal spectrum. The convolution formalism of Munk and Cartwright (1966) models this response function with a Fourier series. This allows the total ocean tide height, and therefore the total ocean tide potential, to be modeled as a weighted sum of past, present, and future values of the tide-generating potential. Previous applications of the convolution formalism have usually been limited to tide height models, but we extend it to ocean tide potential models. We use luni-solar ephemerides to derive the required tide-generating potential so that the complete spectrum of the ocean tide potential is efficiently represented. In contrast, the traditionally adopted harmonic model of the ocean tide potential requires the explicit sum of the contributions from individual tidal frequencies. It is therefore subject to omission errors from neglected frequencies and is computationally more intensive. Intersatellite range rate data from the GRACE mission are used to compare convolution and harmonic models of the ocean tide potential. The monthly range rate residual variance is smaller by 4-5%, and the daily residual variance is smaller by as much as 15% when using the convolution model than when using a harmonic model that is defined by twice the number of parameters.

  13. T-Shaped Emitter Metal Structures for HBTs

    NASA Technical Reports Server (NTRS)

    Fung, King Man; Samoska, Lorene; Velebir, James; Muller, Richard; Echternach, Pierre; Siegel, Peter; Smith, Peter; Martin, Suzanne; Malik, Roger; Rodwell, Mark; Urteaga, Miguel; Paidi, Vamsi; Griffith, Zack

    2006-01-01

    Metal emitter structures in a class of developmental InP-based high-speed heterojunction bipolar transistors (HBTs) have been redesigned to have T-shaped cross sections. T-cross-section metal features have been widely used in Schottky diodes and high-electron-mobility transistors, but not in HBTs. As explained, the purpose served by the present T cross-sectional shapes is to increase fabrication yields beyond those achievable with the prior cross-sectional shapes.

  14. A novel approach for tuberculosis screening based on deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Hwang, Sangheum; Kim, Hyo-Eun; Jeong, Jihoon; Kim, Hee-Jin

    2016-03-01

    Tuberculosis (TB) is one of the major global health threats especially in developing countries. Although newly diagnosed TB patients can be recovered with high cure rate, many curable TB patients in the developing countries are obliged to die because of delayed diagnosis, partly by the lack of radiography and radiologists. Therefore, developing a computer-aided diagnosis (CAD) system for TB screening can contribute to early diagnosis of TB, which results in prevention of deaths from TB. Currently, most CAD algorithms adopt carefully designed morphological features distinguishing different lesion types to improve screening performances. However, such engineered features cannot be guaranteed to be the best descriptors for TB screening. Deep learning has become a majority in machine learning society. Especially in computer vision fields, it has been verified that deep convolutional neural networks (CNN) is a very promising algorithm for various visual tasks. Since deep CNN enables end-to-end training from feature extraction to classification, it does not require objective-specific manual feature engineering. In this work, we designed CAD system based on deep CNN for automatic TB screening. Based on large-scale chest X-rays (CXRs), we achieved viable TB screening performance of 0.96, 0.93 and 0.88 in terms of AUC for three real field datasets, respectively, by exploiting the effect of transfer learning.

  15. Scattering theory for the radial H˙1/2-critical wave equation with a cubic convolution

    NASA Astrophysics Data System (ADS)

    Miao, Changxing; Zhang, Junyong; Zheng, Jiqiang

    2015-12-01

    In this paper, we study the global well-posedness and scattering for the wave equation with a cubic convolution ∂t2 u - Δu = ± (| x | - 3 *| u | 2) u in dimensions d ≥ 4. We prove that if the radial solution u with life-span I obeys (u, ut) ∈ Lt∞ (I ; H˙x 1 / 2 (Rd) × H˙x - 1 / 2 (Rd)), then u is global and scatters. By the strategy derived from concentration compactness, we show that the proof of the global well-posedness and scattering is reduced to disprove the existence of two scenarios: soliton-like solution and high to low frequency cascade. Making use of the No-waste Duhamel formula and double Duhamel trick, we deduce that these two scenarios enjoy the additional regularity by the bootstrap argument of [7]. This together with virial analysis implies the energy of such two scenarios is zero and so we get a contradiction.

  16. Crosswell electromagnetic modeling from impulsive source: Optimization strategy for dispersion suppression in convolutional perfectly matched layer.

    PubMed

    Fang, Sinan; Pan, Heping; Du, Ting; Konaté, Ahmed Amara; Deng, Chengxiang; Qin, Zhen; Guo, Bo; Peng, Ling; Ma, Huolin; Li, Gang; Zhou, Feng

    2016-01-01

    This study applied the finite-difference time-domain (FDTD) method to forward modeling of the low-frequency crosswell electromagnetic (EM) method. Specifically, we implemented impulse sources and convolutional perfectly matched layer (CPML). In the process to strengthen CPML, we observed that some dispersion was induced by the real stretch κ, together with an angular variation of the phase velocity of the transverse electric plane wave; the conclusion was that this dispersion was positively related to the real stretch and was little affected by grid interval. To suppress the dispersion in the CPML, we first derived the analytical solution for the radiation field of the magneto-dipole impulse source in the time domain. Then, a numerical simulation of CPML absorption with high-frequency pulses qualitatively amplified the dispersion laws through wave field snapshots. A numerical simulation using low-frequency pulses suggested an optimal parameter strategy for CPML from the established criteria. Based on its physical nature, the CPML method of simply warping space-time was predicted to be a promising approach to achieve ideal absorption, although it was still difficult to entirely remove the dispersion. PMID:27585538

  17. Toward content-based image retrieval with deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Sklan, Judah E. S.; Plassard, Andrew J.; Fabbri, Daniel; Landman, Bennett A.

    2015-03-01

    Content-based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and eventually, improve patient care. Recent advances in database capacity, algorithm efficiency, and deep Convolutional Neural Networks (dCNN), a machine learning technique, have enabled great CBIR success for general photographic images. Here, we investigate applying the leading ImageNet CBIR technique to clinically acquired medical images captured by the Vanderbilt Medical Center. Briefly, we (1) constructed a dCNN with four hidden layers, reducing dimensionality of an input scaled to 128x128 to an output encoded layer of 4x384, (2) trained the network using back-propagation 1 million random magnetic resonance (MR) and computed tomography (CT) images, (3) labeled an independent set of 2100 images, and (4) evaluated classifiers on the projection of the labeled images into manifold space. Quantitative results were disappointing (averaging a true positive rate of only 20%); however, the data suggest that improvements would be possible with more evenly distributed sampling across labels and potential re-grouping of label structures. This preliminary effort at automated classification of medical images with ImageNet is promising, but shows that more work is needed beyond direct adaptation of existing techniques.

  18. A Systems Level Analysis of Vasopressin-mediated Signaling Networks in Kidney Distal Convoluted Tubule Cells

    PubMed Central

    Cheng, Lei; Wu, Qi; Kortenoeven, Marleen L. A.; Pisitkun, Trairak; Fenton, Robert A.

    2015-01-01

    The kidney distal convoluted tubule (DCT) plays an essential role in maintaining body sodium balance and blood pressure. The major sodium reabsorption pathway in the DCT is the thiazide-sensitive NaCl cotransporter (NCC), whose functions can be modulated by the hormone vasopressin (VP) acting via uncharacterized signaling cascades. Here we use a systems biology approach centered on stable isotope labeling by amino acids in cell culture (SILAC) based quantitative phosphoproteomics of cultured mouse DCT cells to map global changes in protein phosphorylation upon acute treatment with a VP type II receptor agonist 1-desamino-8-D-arginine vasopressin (dDAVP). 6330 unique proteins, containing 12333 different phosphorylation sites were identified. 185 sites were altered in abundance following dDAVP. Basophilic motifs were preferential targets for upregulated sites upon dDAVP stimulation, whereas proline-directed motifs were prominent for downregulated sites. Kinase prediction indicated that dDAVP increased AGC and CAMK kinase families’ activities and decreased activity of CDK and MAPK families. Network analysis implicated phosphatidylinositol-4,5-bisphosphate 3-kinase or CAMKK dependent pathways in VP-mediated signaling; pharmacological inhibition of which significantly reduced dDAVP induced increases in phosphorylated NCC at an activating site. In conclusion, this study identifies unique VP signaling cascades in DCT cells that may be important for regulating blood pressure. PMID:26239621

  19. Analyzing animal behavior via classifying each video frame using convolutional neural networks.

    PubMed

    Stern, Ulrich; He, Ruo; Yang, Chung-Hui

    2015-01-01

    High-throughput analysis of animal behavior requires software to analyze videos. Such software analyzes each frame individually, detecting animals' body parts. But the image analysis rarely attempts to recognize "behavioral states"-e.g., actions or facial expressions-directly from the image instead of using the detected body parts. Here, we show that convolutional neural networks (CNNs)-a machine learning approach that recently became the leading technique for object recognition, human pose estimation, and human action recognition-were able to recognize directly from images whether Drosophila were "on" (standing or walking) or "off" (not in physical contact with) egg-laying substrates for each frame of our videos. We used multiple nets and image transformations to optimize accuracy for our classification task, achieving a surprisingly low error rate of just 0.072%. Classifying one of our 8 h videos took less than 3 h using a fast GPU. The approach enabled uncovering a novel egg-laying-induced behavior modification in Drosophila. Furthermore, it should be readily applicable to other behavior analysis tasks.

  20. Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection

    PubMed Central

    Gottschlich, Carsten

    2016-01-01

    We present a new type of local image descriptor which yields binary patterns from small image patches. For the application to fingerprint liveness detection, we achieve rotation invariant image patches by taking the fingerprint segmentation and orientation field into account. We compute the discrete cosine transform (DCT) for these rotation invariant patches and attain binary patterns by comparing pairs of two DCT coefficients. These patterns are summarized into one or more histograms per image. Each histogram comprises the relative frequencies of pattern occurrences. Multiple histograms are concatenated and the resulting feature vector is used for image classification. We name this novel type of descriptor convolution comparison pattern (CCP). Experimental results show the usefulness of the proposed CCP descriptor for fingerprint liveness detection. CCP outperforms other local image descriptors such as LBP, LPQ and WLD on the LivDet 2013 benchmark. The CCP descriptor is a general type of local image descriptor which we expect to prove useful in areas beyond fingerprint liveness detection such as biological and medical image processing, texture recognition, face recognition and iris recognition, liveness detection for face and iris images, and machine vision for surface inspection and material classification. PMID:26844544